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Brais Cancela

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Rose-Ann Bigué

Faculty of Engineering of the University of Porto

Claudia Cavallaro

Università di Catania

Abdulrahman Al-Molegi

Erwin Dwika Putra

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Papers by Brais Cancela

Mining Human Mobility Data to Discover Locations and Habits

arXiv (Cornell University), Sep 25, 2019

Many aspects of life are associated with places of human mobility patterns and nowadays we are fa... more Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

Uso de técnicas de saliency para selección de características

XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018) 23-26 de octubre de 2018 Granada, España: Avances en Inteligencia Artificial, 2018, ISBN 978-84-09-05643-9, págs. 1231-1236, 2018

101 - A Delayed Elastic-Net Approach for Performing Adversarial Attacks

ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/122/nn Abstract: With the rise of t... more ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/122/nn Abstract: With the rise of the so-called Adversarial Attacks, there is an increased concern on model security. In this paper we present two different contributions: novel measures of robustness (based on adversarial attacks) and a novel adversarial attack. The key idea behind these metrics is to obtain a measure that could compare different architectures, with independence of how the input is preprocessed (robustness against different input sizes and value ranges). To do so, a novel adversarial attack is presented, performing a delayed elastic-net adversarial attack (constraints are only used whenever a successful adversarial attack is obtained). Experimental results show that our approach obtains state-of-the-art adversarial samples, in terms of minimal perturbation distance. Finally, a benchmark of ImageNet pretrained models is used to conduct experiments aiming to shed some light about which model should be sele...

Understanding target trajectory behavior: a dynamic scene modeling approach

El analisis de comportamiento humano es uno de los campos mas activos en la rama de vision por co... more El analisis de comportamiento humano es uno de los campos mas activos en la rama de vision por computador. Con el incremento de camaras, especialmente en entornos controlados tales como aeropuertos, estaciones de tren o museos, se hace cada vez mas necesario el uso de sistemas automaticos que puedan catalogar la informacion proporcionada. En el caso de entornos concurridos, es muy dificil el poder distinguir el comportamiento de personas en base a sus gestos, debido a la falta de vision de su cuerpo al completo. Por ende, el analisis de comportamiento se realiza en base a sus trayectorias, anadiendo tecnicas de razonamiento de alto nivel para ulilizar dicha informacion en multiples aplicaciones, tales como la video vigilancia o el analisis de trafico. El proposito de esta investigacion es el desarrollo de un sistema totalmente automatico para el analisis de comportamiento de las personas. Por una parte, se presentan dos sistemas para el seguimiento de multiples objetivos, asi como u...

104 - Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss

ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/102/nn Abstract: Traditionally, a N... more ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/102/nn Abstract: Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other classes. There are no restrictions on the spatial placement of deep features (last layer input). This paper addresses this issue when dealing with Neural Networks, providing a set of loss functions that are able to train a classifier by forcing the deep features to be projected over a predefined orthogonal basis. Experimental results shows that these `data placement' functions can overcome the training accuracy provided by the classic cross-entropy loss function.

Feature Selection: From the Past to the Future

Learning and Analytics in Intelligent Systems, 2022

Automatic Analysis of the Patient's Conscious Responses to the Emission of Auditory Stimuli during the Performance of an Audiometry

2011 International Conference on Digital Image Computing: Techniques and Applications, 2011

This paper presents a methodology for the automatic detection of human conscious responses to dif... more This paper presents a methodology for the automatic detection of human conscious responses to different auditory stimuli during the performance of an audiogram. An audiogram is the standard way of analyze the hearing of a patient in order to diagnose hearing loss. From a video sequence captured during the hearing evaluation performed by an expert to a patient, the methodology will be able to detect the instants in which the expert is sending auditory stimuli and when the patient responds consciously to them by raising his hand in order to indicate that he has heard them. The proposed method has been tested on a dataset of almost 7000 frames from three different video sequences obtaining very encouraging results in the hand raise detection as a response to the auditory stimuli. Finally, an analysis of the patient's speed reaction to stimuli is discussed.

Contextual and Skin Color Region Information for Face and Arms Location

This paper presents a methodology for the location of arms and faces combining color information ... more

Automatic Eye Gesture Recognition in Audiometries for Patients with Cognitive Decline

Lecture Notes in Computer Science, 2013

This paper provides a specifically adapted methodology for supporting the audiologists when testi... more This paper provides a specifically adapted methodology for supporting the audiologists when testing the hearing of patients with cognitive decline or other communication disabilities. These patients can not interact with the audiologist conventionally, but they often express gestural reactions when they perceive the auditory stimuli typically associated to the eyes region. From a video sequence captured during the hearing evaluation, we analyze the movements in the area of the patient's eyes, so we can detect these gestural reactions. We define a set of different gestures for classification, based on the expert knowledge. The proposed method achieves an accuracy of the 90.65% when classifying these movements, showing their separability, and therefore, the possibility of interpreting them with high-level information as positive reactions to the auditory stimuli.

Movement Analysis for the Assessment of Hearing in Patients with Cognitive Impairment: A Preliminary Study

Lecture Notes in Computer Science, 2013

Patients with severe cognitive impairment are not capable of interacting with the audiologist dur... more Patients with severe cognitive impairment are not capable of interacting with the audiologist during an audiology test. However, these patients often show gestural reaction to the auditory stimuli that can be interpreted by audiologists with comprehensive experience in this type of case. In this study, we analyze the area around the eyes and the movements that occur within it, since is in this area where most of gestural reactions take place for these patients.

Measuring response times to auditory stimuli during an audiometry

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11, 2011

ABSTRACT This paper provides a methodology for the measure of the patient’s reaction times to aud... more ABSTRACT This paper provides a methodology for the measure of the patient’s reaction times to auditory stimuli during the performance of an audiometry. An audiometry is the standard way of analyzing the hearing of a patient in order to diagnose hearing loss. From a video sequence recorded during this test, the method will be able to detect the instants when the expert is sending the auditory stimulus and when the patient responds consciously to it by raising his hand, being able, this way, to measure its reaction time. The proposed method was tested on several video sequences from different individuals yielding highly accurate results. The possibility of quantitative measure the reaction times will allow the experts to conduct several studies and to further complete the evaluation of their patients.

Path Analysis in Multiple-Target Video Sequences

Lecture Notes in Computer Science, 2011

Path analysis becomes a powerful tool when dealing with behavior analysis, i. e., detecting abnor... more Path analysis becomes a powerful tool when dealing with behavior analysis, i. e., detecting abnormal movements. In a multiple target scenario it is complicated to obtain each object path because of collision events, such as grouping and splitting targets, and occlusions, both total or partial. In this work, a method to obtain the similarity between different trajectories is presented, based in register techniques. In addition, an hierarchical architecture is used to obtain the corresponding paths of the objects in a scene, to cope with collision events. Experimental results show promising results in path analysis, enabling it to establish thresholds to abnormal path detection.

Solving Multiple-Target Tracking Using Adaptive Filters

Lecture Notes in Computer Science, 2011

Multiple-target tracking represents a challenging question in uncontrolled scenarios. Due to high... more Multiple-target tracking represents a challenging question in uncontrolled scenarios. Due to high-level applications, such as behavioral analysis, the need of a robust tracking system is high. In a multiple tracking scenario it is necessary to consider and resolve occlusions, as well as formations and splitting of object groups. In this work, a method based in a hierarchical architecture for multiple

Unsupervised Trajectory Modelling Using Temporal Information via Minimal Paths

2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based... more This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before. Thus, a minimal path approach can be used to model human trajectory behaviour. We develop a modified Fast Marching Method that allows us to include both velocity and orientation in the Front Propagation Approach, without increasing its computational complexity. Combining all the information, we create a time surface that shows the time a target need to reach any given position in the scene. We also create different metrics in order to compare the time surface against the real behaviour. Experimental results over a public dataset prove the initial hypothesis' correctness.

On the use of a minimal path approach for target trajectory analysis

Pattern Recognition, 2013

ABSTRACT Vision-based action recognition has multiple applications, mainly focused in video surve... more ABSTRACT Vision-based action recognition has multiple applications, mainly focused in video surveillance systems. The art of labeling each target behavior in crowded scenarios is a complicated field since usually we do not have visual confirmation of the parts of a target to infer its behavior. Thus, trajectory analysis becomes a good choice to try to infer knowledge about target movements. Most of the contributions to this field involve a training period in which we obtain information a priori about the environment, storing a dataset with all the possible usual routes. Based in the minimal path theory using geodesic active contours, we present a novel architecture where no initial information about the scene is needed, while it is possible to include it if necessary to specify constraints. Experimental results in two different application domains show the performance and flexibility of this method, being able to be used in multiple trajectory analysis problems.

Multiple human tracking system for unpredictable trajectories

Machine Vision and Applications, 2013

ABSTRACT Tracking multiple objects into a scene is one of the most active research topics in comp... more ABSTRACT Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.

Automatic processing of audiometry sequences for objective screening of hearing loss

Expert Systems with Applications, 2012

ABSTRACT Audiology is the branch of science that deals with hearing, balance, and related disorde... more ABSTRACT Audiology is the branch of science that deals with hearing, balance, and related disorders. Detecting patients with slow responses to auditory stimuli is relevant because this slowness could be due to other cognitive problems or conditions, which should be studied carefully. In this paper, we present an automatic methodology for processing video sequences recorded during the performance of hearing tests to patients. This screening method allows us to measure the patient’s response times to the auditory stimuli sent to them, and based on these times, to identify those patients with response times abnormally slow. The method is tested on individuals taken at random from a standard population, and based on the obtained results, it is confirmed that the proposed method is valid for the automatic detection of patients with slow response times, and it also serves to the experts as a tool for the accurate and objective measurement of these times.

Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios

Expert Systems with Applications, 2013

Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenario... more Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenarios. Two common approaches are often used, one based on low-level techniques to detect each object size, position and velocity, and other based on high-level techniques that deal with object appearance. None of these methods can deal with all possible problems in multiple-target tracking: environment occlusions, both total and partial, and collisions, such as grouping and splitting events. So one solution is to merge these techniques to improve their performance. Based on an existing hierarchical architecture, we present a novel technique that can deal with all the mentioned problems in multiple tracking targets. Blob detection, low-level tracking using adaptive filters, high-level tracking based on a fixed pool of histograms and an event management that can detect every collision event and performs occlusion recovery are used to be able to track every object during the time they appear within the scene. Experimental results show the performance of this technique under multiple situations, being able to track every object in the scene without losing their initial identification. The speed processing is higher than 50 frames, which allows it to be used under real-time scenarios.

Trajectory Similarity Measures Using Minimal Paths

Lecture Notes in Computer Science, 2013

Dealing with surveillance systems, large amount of distance measures are presented in order to cl... more Dealing with surveillance systems, large amount of distance measures are presented in order to classify both normal and abnormal behavior. Typically, techniques based in point-to-point distances are used. However, these techniques do not take into account information about the environment, like pits or restricted areas, for instance. Using a minimal path algorithm to model the usual paths, we develop new trajectory distance measures that are able to introduce information about the scene. The results obtained show promising results.

Open-World Person Re-Identification by Multi-Label Assignment Inference

Person re-identification methods have recently made tremendous progress on max-imizing re-identif... more Person re-identification methods have recently made tremendous progress on max-imizing re-identification accuracy between camera pairs. However, this line of work mostly shares an critical limitation- it assumes re-identification in a ‘closed world’. That is, between a known set of people who all appear in both views of a single pair of cam-eras. This is clearly far from a realistic application scenario. In this study, we take a significant step toward a more realistic ‘open world ’ scenario. We consider associating persons observed in more than two cameras where: multiple within-camera detections are possible; different people can transit between different cameras – so that there is only partial and unknown overlap of identity between people observed by each camera; and the total number of unique people among all cameras is itself unknown. To address this significantly more challenging open world scenario, we propose a novel framework based on online Conditional Random Field (CRF) ...

Mining Human Mobility Data to Discover Locations and Habits

arXiv (Cornell University), Sep 25, 2019

Many aspects of life are associated with places of human mobility patterns and nowadays we are fa... more Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

Uso de técnicas de saliency para selección de características

XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018) 23-26 de octubre de 2018 Granada, España: Avances en Inteligencia Artificial, 2018, ISBN 978-84-09-05643-9, págs. 1231-1236, 2018

101 - A Delayed Elastic-Net Approach for Performing Adversarial Attacks

ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/122/nn Abstract: With the rise of t... more ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/122/nn Abstract: With the rise of the so-called Adversarial Attacks, there is an increased concern on model security. In this paper we present two different contributions: novel measures of robustness (based on adversarial attacks) and a novel adversarial attack. The key idea behind these metrics is to obtain a measure that could compare different architectures, with independence of how the input is preprocessed (robustness against different input sizes and value ranges). To do so, a novel adversarial attack is presented, performing a delayed elastic-net adversarial attack (constraints are only used whenever a successful adversarial attack is obtained). Experimental results show that our approach obtains state-of-the-art adversarial samples, in terms of minimal perturbation distance. Finally, a benchmark of ImageNet pretrained models is used to conduct experiments aiming to shed some light about which model should be sele...

Understanding target trajectory behavior: a dynamic scene modeling approach

El analisis de comportamiento humano es uno de los campos mas activos en la rama de vision por co... more El analisis de comportamiento humano es uno de los campos mas activos en la rama de vision por computador. Con el incremento de camaras, especialmente en entornos controlados tales como aeropuertos, estaciones de tren o museos, se hace cada vez mas necesario el uso de sistemas automaticos que puedan catalogar la informacion proporcionada. En el caso de entornos concurridos, es muy dificil el poder distinguir el comportamiento de personas en base a sus gestos, debido a la falta de vision de su cuerpo al completo. Por ende, el analisis de comportamiento se realiza en base a sus trayectorias, anadiendo tecnicas de razonamiento de alto nivel para ulilizar dicha informacion en multiples aplicaciones, tales como la video vigilancia o el analisis de trafico. El proposito de esta investigacion es el desarrollo de un sistema totalmente automatico para el analisis de comportamiento de las personas. Por una parte, se presentan dos sistemas para el seguimiento de multiples objetivos, asi como u...

104 - Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss

ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/102/nn Abstract: Traditionally, a N... more ICPR Browser Link: https://ailb-web.ing.unimore.it/icpr/paper/102/nn Abstract: Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other classes. There are no restrictions on the spatial placement of deep features (last layer input). This paper addresses this issue when dealing with Neural Networks, providing a set of loss functions that are able to train a classifier by forcing the deep features to be projected over a predefined orthogonal basis. Experimental results shows that these `data placement' functions can overcome the training accuracy provided by the classic cross-entropy loss function.

Feature Selection: From the Past to the Future

Learning and Analytics in Intelligent Systems, 2022

Automatic Analysis of the Patient's Conscious Responses to the Emission of Auditory Stimuli during the Performance of an Audiometry

2011 International Conference on Digital Image Computing: Techniques and Applications, 2011

This paper presents a methodology for the automatic detection of human conscious responses to dif... more This paper presents a methodology for the automatic detection of human conscious responses to different auditory stimuli during the performance of an audiogram. An audiogram is the standard way of analyze the hearing of a patient in order to diagnose hearing loss. From a video sequence captured during the hearing evaluation performed by an expert to a patient, the methodology will be able to detect the instants in which the expert is sending auditory stimuli and when the patient responds consciously to them by raising his hand in order to indicate that he has heard them. The proposed method has been tested on a dataset of almost 7000 frames from three different video sequences obtaining very encouraging results in the hand raise detection as a response to the auditory stimuli. Finally, an analysis of the patient's speed reaction to stimuli is discussed.

Contextual and Skin Color Region Information for Face and Arms Location

This paper presents a methodology for the location of arms and faces combining color information ... more

Automatic Eye Gesture Recognition in Audiometries for Patients with Cognitive Decline

Lecture Notes in Computer Science, 2013

This paper provides a specifically adapted methodology for supporting the audiologists when testi... more This paper provides a specifically adapted methodology for supporting the audiologists when testing the hearing of patients with cognitive decline or other communication disabilities. These patients can not interact with the audiologist conventionally, but they often express gestural reactions when they perceive the auditory stimuli typically associated to the eyes region. From a video sequence captured during the hearing evaluation, we analyze the movements in the area of the patient's eyes, so we can detect these gestural reactions. We define a set of different gestures for classification, based on the expert knowledge. The proposed method achieves an accuracy of the 90.65% when classifying these movements, showing their separability, and therefore, the possibility of interpreting them with high-level information as positive reactions to the auditory stimuli.

Movement Analysis for the Assessment of Hearing in Patients with Cognitive Impairment: A Preliminary Study

Lecture Notes in Computer Science, 2013

Patients with severe cognitive impairment are not capable of interacting with the audiologist dur... more Patients with severe cognitive impairment are not capable of interacting with the audiologist during an audiology test. However, these patients often show gestural reaction to the auditory stimuli that can be interpreted by audiologists with comprehensive experience in this type of case. In this study, we analyze the area around the eyes and the movements that occur within it, since is in this area where most of gestural reactions take place for these patients.

Measuring response times to auditory stimuli during an audiometry

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11, 2011

ABSTRACT This paper provides a methodology for the measure of the patient’s reaction times to aud... more ABSTRACT This paper provides a methodology for the measure of the patient’s reaction times to auditory stimuli during the performance of an audiometry. An audiometry is the standard way of analyzing the hearing of a patient in order to diagnose hearing loss. From a video sequence recorded during this test, the method will be able to detect the instants when the expert is sending the auditory stimulus and when the patient responds consciously to it by raising his hand, being able, this way, to measure its reaction time. The proposed method was tested on several video sequences from different individuals yielding highly accurate results. The possibility of quantitative measure the reaction times will allow the experts to conduct several studies and to further complete the evaluation of their patients.

Path Analysis in Multiple-Target Video Sequences

Lecture Notes in Computer Science, 2011

Path analysis becomes a powerful tool when dealing with behavior analysis, i. e., detecting abnor... more Path analysis becomes a powerful tool when dealing with behavior analysis, i. e., detecting abnormal movements. In a multiple target scenario it is complicated to obtain each object path because of collision events, such as grouping and splitting targets, and occlusions, both total or partial. In this work, a method to obtain the similarity between different trajectories is presented, based in register techniques. In addition, an hierarchical architecture is used to obtain the corresponding paths of the objects in a scene, to cope with collision events. Experimental results show promising results in path analysis, enabling it to establish thresholds to abnormal path detection.

Solving Multiple-Target Tracking Using Adaptive Filters

Lecture Notes in Computer Science, 2011

Multiple-target tracking represents a challenging question in uncontrolled scenarios. Due to high... more Multiple-target tracking represents a challenging question in uncontrolled scenarios. Due to high-level applications, such as behavioral analysis, the need of a robust tracking system is high. In a multiple tracking scenario it is necessary to consider and resolve occlusions, as well as formations and splitting of object groups. In this work, a method based in a hierarchical architecture for multiple

Unsupervised Trajectory Modelling Using Temporal Information via Minimal Paths

2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based... more This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before. Thus, a minimal path approach can be used to model human trajectory behaviour. We develop a modified Fast Marching Method that allows us to include both velocity and orientation in the Front Propagation Approach, without increasing its computational complexity. Combining all the information, we create a time surface that shows the time a target need to reach any given position in the scene. We also create different metrics in order to compare the time surface against the real behaviour. Experimental results over a public dataset prove the initial hypothesis' correctness.

On the use of a minimal path approach for target trajectory analysis

Pattern Recognition, 2013

ABSTRACT Vision-based action recognition has multiple applications, mainly focused in video surve... more ABSTRACT Vision-based action recognition has multiple applications, mainly focused in video surveillance systems. The art of labeling each target behavior in crowded scenarios is a complicated field since usually we do not have visual confirmation of the parts of a target to infer its behavior. Thus, trajectory analysis becomes a good choice to try to infer knowledge about target movements. Most of the contributions to this field involve a training period in which we obtain information a priori about the environment, storing a dataset with all the possible usual routes. Based in the minimal path theory using geodesic active contours, we present a novel architecture where no initial information about the scene is needed, while it is possible to include it if necessary to specify constraints. Experimental results in two different application domains show the performance and flexibility of this method, being able to be used in multiple trajectory analysis problems.

Multiple human tracking system for unpredictable trajectories

Machine Vision and Applications, 2013

ABSTRACT Tracking multiple objects into a scene is one of the most active research topics in comp... more ABSTRACT Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.

Automatic processing of audiometry sequences for objective screening of hearing loss

Expert Systems with Applications, 2012

ABSTRACT Audiology is the branch of science that deals with hearing, balance, and related disorde... more ABSTRACT Audiology is the branch of science that deals with hearing, balance, and related disorders. Detecting patients with slow responses to auditory stimuli is relevant because this slowness could be due to other cognitive problems or conditions, which should be studied carefully. In this paper, we present an automatic methodology for processing video sequences recorded during the performance of hearing tests to patients. This screening method allows us to measure the patient’s response times to the auditory stimuli sent to them, and based on these times, to identify those patients with response times abnormally slow. The method is tested on individuals taken at random from a standard population, and based on the obtained results, it is confirmed that the proposed method is valid for the automatic detection of patients with slow response times, and it also serves to the experts as a tool for the accurate and objective measurement of these times.

Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios

Expert Systems with Applications, 2013

Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenario... more Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenarios. Two common approaches are often used, one based on low-level techniques to detect each object size, position and velocity, and other based on high-level techniques that deal with object appearance. None of these methods can deal with all possible problems in multiple-target tracking: environment occlusions, both total and partial, and collisions, such as grouping and splitting events. So one solution is to merge these techniques to improve their performance. Based on an existing hierarchical architecture, we present a novel technique that can deal with all the mentioned problems in multiple tracking targets. Blob detection, low-level tracking using adaptive filters, high-level tracking based on a fixed pool of histograms and an event management that can detect every collision event and performs occlusion recovery are used to be able to track every object during the time they appear within the scene. Experimental results show the performance of this technique under multiple situations, being able to track every object in the scene without losing their initial identification. The speed processing is higher than 50 frames, which allows it to be used under real-time scenarios.

Trajectory Similarity Measures Using Minimal Paths

Lecture Notes in Computer Science, 2013

Dealing with surveillance systems, large amount of distance measures are presented in order to cl... more Dealing with surveillance systems, large amount of distance measures are presented in order to classify both normal and abnormal behavior. Typically, techniques based in point-to-point distances are used. However, these techniques do not take into account information about the environment, like pits or restricted areas, for instance. Using a minimal path algorithm to model the usual paths, we develop new trajectory distance measures that are able to introduce information about the scene. The results obtained show promising results.

Open-World Person Re-Identification by Multi-Label Assignment Inference

Person re-identification methods have recently made tremendous progress on max-imizing re-identif... more Person re-identification methods have recently made tremendous progress on max-imizing re-identification accuracy between camera pairs. However, this line of work mostly shares an critical limitation- it assumes re-identification in a ‘closed world’. That is, between a known set of people who all appear in both views of a single pair of cam-eras. This is clearly far from a realistic application scenario. In this study, we take a significant step toward a more realistic ‘open world ’ scenario. We consider associating persons observed in more than two cameras where: multiple within-camera detections are possible; different people can transit between different cameras – so that there is only partial and unknown overlap of identity between people observed by each camera; and the total number of unique people among all cameras is itself unknown. To address this significantly more challenging open world scenario, we propose a novel framework based on online Conditional Random Field (CRF) ...