Object Tracking

IEEE International Conference on Image Processing 2005, 2005

In this paper, a multiple-object tracking method for visual surveillance applications is presented. Moving objects are detected by adaptive background subtraction and tracked by using a multi-hypothesis testing approach. Object matching between frames is done based on proximity and appearance similarity. A new confidence measure is assigned to each possible match. This information is arranged into a graph structure where vertices represent blobs in consecutive frames and edges represent match confidence values. This graph is later used to prune and refine trajectories to obtain the salient object trajectories. Occlusions are handled through position prediction using Kalman filter and robust color similarity measures. Proposed framework is able to handle imperfections in moving object detection such as spurious objects, fragmentation, shadow, clutter and occlusions.

In this paper, a multiple-object tracking method for visual surveillance applications is presented. Moving objects are detected by adaptive background subtraction and tracked by using a multi-hypothesis testing approach. Object matching between frames is done based on proximity and appearance similarity. A new confidence measure is assigned to each possible match. This information is arranged into a graph structure where vertices represent blobs in consecutive frames and edges represent match confidence values. This graph is later used to prune and refine trajectories to obtain the salient object trajectories. Occlusions are handled through position prediction using Kalman filter and robust color similarity measures. Proposed framework is able to handle imperfections in moving object detection such as spurious objects, fragmentation, shadow, clutter and occlusions.

In PC vision, the most dynamic exploration points are visual reconnaissance in element scenes, particularly for people & vehicles. Wide range of promising applications incorporating human identification at some distance, controlling access in extraordinary ranges, measurements of group flux and investigating blockage or odd particles and for the utilization of numerous cameras intelligent reconnaissance and so much more. Visual observations in element scenes in the handling system incorporates various taking after stages i.e. characterization of moving item, depiction of the comprehensive particles, identifying the movement, displaying the whole situation, proof of human identification, at the end combining the information from different cameras. There are mixes of 2D & 3D images, therefore recognizing abnormalities and conducting forecast so that substance based recovery of reconnaissance features can be done. There are more things to understand about these like, common dialect portrayal, data combination from various sensors.

– Computer Vision is the part of " Artificial Intelligence " concerned with the theory behind artificial systems that extract information from images. Within which, Video Surveillance is a term given to monitor the behavior of any kind through videos. It requires person to monitor the CCTV and huge volume of memory to record it. One of the major challenges involved is the huge volume of video storage and retrieval of the same on demand. In order to avoid the depletion of human resources and to detect the suspicious behaviors that threaten safety and security, Intelligent Video Surveillance system (IVS) is required. The proposed work is focused on bringing effective and efficient video surveillance system with added intelligence to avoid human intervention in identifying security threats. In IVS, Extended Kalman filters the Gaussian mixture models are used to detect the moving objects. A tracking algorithm is proposed for tracking the moving objects. It implements position of each group, the recognition of the same group, and the newly appearing and disappearing groups. So, the proposed work IVS, promises the robustness against the environmental influences and speed, which are suitable for the real-time surveillance in detecting and tracking moving objects.

Detection and tracking are two important aspects of visual surveillance applications which is gaining importance rapidly. Certain conditions like cluttered background, camera noise, target appearance variation, and occlusion are barriers to robust detection and tracking, especially in case of multiple moving objects. Object detection under complex backgrounds is an area of active research. Contrary to recent approaches, this paper focus on developing a framework which can effectively detect multiple moving objects and track them despite of background clutter and prior knowledge of targets in the scene with two major contributions. First, a segmentation method which is robust in dynamic background conditions such as swaying leaves, fountains, and other complex backgrounds. Second, a multiple object tracking algorithm using Kalman filter which perform efficient tracking of occluded objects. By modelling the background and foreground, the system can accurately detect the real moving objects. The video object tracking method assigns each object a unique track and maintains it over time. Experimental results convey that the framework perform well for several challenging sequences, and our proposed framework is effective for the aforementioned challenges.

The Fourth International Conference on Computer Science Engineering and Applications, 2014

Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult problem, especially in case of multiple moving objects. Object detection in the presence of camera noise and with variable or unfavourable luminance conditions is still an active area of research. This paper proposes a framework which can effectively detect the moving objects and track them despite of occlusion and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision algorithm which uses a multi-background model. The video object tracking is able to track multiple objects along with their trajectories based on Continuous Energy Minimization. In this work, an effective formulation of multi-target tracking as minimization of a continuous energy is combined with multi-background registration. Apart from the recent approaches, it focus on making use of an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function considers physical constraints, such as target dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track multiple objects despite of occlusions under dynamic background conditions.

BT Technology Journal, 2004

This paper aims to address two of the key research issues in computer vision -the detection and tracking of multiple objects in the cluttered dynamic scene -that underpin the intelligence aspects of advanced visual surveillance systems aiming at automated visual events detection and behaviour analysis. We discuss two major contributions in resolving these problems within a systematic framework. Firstly, for accurate object detection, an efficient and effective scheme is proposed to remove cast shadows/highlights with error corrections based on a conditional morphological reconstruction. Secondly, for effective tracking, a temporal-template-based tracking scheme is introduced, using multiple descriptive cues (velocity, shape, colour, etc) of the 2-D object appearance together with their respective variances over time. A scaled Euclidean distance is used as the matching metric, and the template is updated using Kalman filters when a matching is found or by linear mean prediction in the case of occlusion. Extensive experiments are carried out on video sequences from various realworld scenarios. The results show very promising tracking performance.

IRJET, 2022

One of the current demanding research problems in computer vision is visual surveillance in dynamic situations, particularly for humans and automobiles. It is a critical technology in the battle against terrorism, crime, public safety, and effective traffic management. The endeavor entails the creation of an effective visual surveillance system for use in complex contexts. Detecting moving objects from a video is critical for object categorization, target tracking, activity recognition, and behavior understanding in video surveillance. The first relevant phase of information is the detection of moving objects in video streams, and background subtraction is a typical method for foreground segmentation. We simulated background subtraction and Self-Organizing Background Subtraction (SOBS) approaches in this paper. Establishing a credible background updating model based on statistical data and using a dynamic optimization threshold method to produce a more complete moving object is the goal of Background Subtraction. In SOBS, a method for detecting moving objects is based on a neural background model that is automatically produced via a self-organizing mechanism.

2016

Detecting and tracking objects in crowded areas is a challenging issue in the field of Video Surveillance System. Nowadays the increase of digital video cameras, and the availability of video storage and high performance video processing hardware, opens up conceivable outcomes for tackling many video understanding problems. Developing a real-time video understanding technique which can process the large amounts of data becomes very important. The object detection first step used in surveillance applications aims to separation of foreground objects from the background. Many algorithms proposed to solve the problem of object detection, however, it still lack of tracking multiple objects in real time. Object tracking used to find a moving object detected in motion detection stage from one frame to another in an image sequence. This paper focuses on review of various techniques used in object detection and object tracking.