Pedestrians Tracking Using Offboard Cameras

Driver Assistance Systems are becoming more common for safety systems in the automotive environment. With respect to road accident statistics, on-board pedestrian detection is a key task for future driver assistance systems. In this paper, we describes a system based on image processing to help the driver in these situations. Here a video camera is mounted in front of the vehicle and each frames from the video file is analyzed to take the proper decision. This system describes an image processing algorithm based on three modules - ROI generation, object classification based on HOG features and Kalman filter tracking cascaded together, and each module uses visual features to identify objects and classify each object as pedestrians from the cluttered background in the range of 20-50m. ROI generation is performed using adaptive thresholding technique based on the common fact that the gray images will have objects appearing brighter than the surrounding background. The suitable candidates are selected on the basis of various factors like height, width, aspect ratio etc. A two stage AdaBoost object classifier utilizing HAAR like and HOG feature extraction methods is described here. Adaboost is a learning algorithm building a stronger classifier combining many weak classifier with weighted majority vote. For tracking of selected pedestrians, a kalman filter based object tracking is employed. A template matching is also used in case of errors in kalman based object tracking. Matlab is used for the simulation part and the proposed algorithm works accurately with various lighting conditions and is suitable for practical applications.

Recent Patents on Engineering, 2022

Background: Pedestrian detection and tracking are an important area of study in realworld applications, such as mobile robots, human-computer interaction, video surveillance, pedestrian protection systems, etc. As a result, it has attracted the interest of the scientific community. Objective: Certainly, tracking people is critical for numerous utility areas which cover unusual situations detection, like vicinity evaluation, and sometimes change direction in human gait and partial occlusions. Researchers' primary focus is to develop a surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. So, it has become a significant issue and challenge to design a tracking system that can be more suitable for such situations. To this end, this paper presents a comparative evaluation of the tracking-by-detection system along with the publicly available pedestrian benchmark databases. Method: Unlike recent works where person detection and tracking are usually treated separately, our work explores the joint use of the popular Simple Online and Real-time Tracking (SORT) method and the relevant visual detectors. Consequently, the choice of the detector is an important factor in the evaluation of the system's performance. Results: Experimental results demonstrate that the performance of the tracking-by-detection system is closely related to the optimal selection of the detector and should be required prior to a rigorous evaluation. Conclusion: The study demonstrates how sensitive the system performance as a whole is to the challenges of the dataset. Furthermore, the efficiency of the detector and the detector-tracker combination is also depending on the dataset.

International Journal of Advanced Robotic Systems, 2020

Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.

2011

Road safety applications require the most reliable data. In recent years data fusion is becoming one of the main technologies for Advance Driver Assistant Systems (ADAS) to overcome the limitations of isolated use of the available sensors and to fulfil demanding safety requirements. In this paper a real application of data fusion for road safety for pedestrian detection is presented. Two sets of automobile-emplaced sensors are used to detect pedestrians in urban environments, a laser scanner and a stereovision system. Both systems are mounted in the automobile research platform IVVI 2.0 to test the algorithms in real situations. The different safety issues necessary to develop this fusion application are described. Context information such as velocity and GPS information is also used to provide danger estimation for the detected pedestrians.

2010 13th International Conference on Information Fusion, 2010

Signals and Systems …, 2009

2005

This paper describes research on several aspects of pedestrian detection and tracking being carried out at Napier University. A description is given of measurements of pedestrian trajectories made with low-cost, low resolution thermal imagers, produced by Irisys (Infrared Integrated Systems Ltd). These detectors provide accurate measurements over an area of approximately 10 square metres. We report on work that attempts

Ingénierie des systèmes d information

As indicated by the Transportation Research and Industry Prevention Programme (TRIPP)'s Road Safety in India Report-2020, 33% of the accidents victims (deaths) are pedestrians. Heavy vehicles as well as cars are not able track pedestrian's movements on time. Most of the Children met with the accidents due to vehicle reversing. This problem motivates to track pedestrian through rear-view in heavy vehicles as well as for cars. Certain machine learning and deep learning approaches will best adapt to coping with the particular problems of rear-view pedestrian detection. In this work a literature survey of pedestrian detection and tracking research methodology and their constraints are discussed briefly. Most of the camera applications mainly concentrate on picture visibility and tracking. If the pedestrian detection application makes as inbuilt technique, then automatically so many accidents especially of children can be avoided. This pedestrian application mainly used to track the pedestrian movements while he or she is moving on heavy traffic roads and highways or while taking vehicle reverse by using cameras which were fixed on vehicles and make alerts. Such that camera can get more extracted features and helps the future applications. In this research paper a brief literature review is placed according to various researchers along with their techniques. And also compare the performance measures such as accuracy, sensitivity, false alarm rate and detection rate. These experimental results are out performance the methodology and differentiated with present technology.

2000

Abstract Intelligent vehicles and unattended driving systems of the future will need the ability to recognize relevant traffic participants (such as other vehicles, pedestrians, bicyclists, etc.) and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. The associated computer vision problem that needs to be solved is detection and tracking of pedestrians from a moving camera, which is extremely challenging.

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009

Because pedestrians have neither well defined shapes nor well defined behaviors, detecting and tracking them from a moving vehicle remains a difficult task. To serve as an onboard driver assistance system, a perception algorithm also needs to be both fast and robust. We present in this paper a system that reaches a good level of reliability by efficiently combining the data of two sensors -a laser scanner and a camera -while remaining tractable on CPU limited mobile architectures.

Lecture Notes in Computer Science, 1997

In computer vision real-time tracking of moving objects in natural scenes has become more and more important. In this paper we describe a complete system for data driven tracking of moving objects. We apply the system to tracking pedestrians in natural scenes. No specialized hardware is used. To achieve the necessary efficiency several principles of active vision, namely selection in space, time, and resolution are implemented. For object tracking, a contour based approach is used which allows contour extraction and tracking within the image frame rate on general purpose architectures. A pan/tilt camera is steered by a camera control module to pursue the moving object. A dedicated attention module is responsible for the robustness of the complete system. The experiments over several hours prove the robustness and accuracy of the whole system. Tracking of pedestrians in a natural scene has been successful in 79% of the time.

Advanced Video and Signal Based Surveillance, 2006

We show that counting accuracies up to 98 % can be achieved.

2007

This paper describes an improved stereo vision system for anticipated detection of car-to-pedestrian accidents. An improvement of previous versions of the pedestrian dection system is achieved by compensation of the cameras pitch angle, since it results in higher accuracy in the location of the ground plane and more accurate depth measurements. The pedestrian detection system has been applied to collision avoidance and mitigation. Collision avoidance is carried out by means of deceleration strategies, whenever the accident is evitable. Likewise, collision mitigation is accomplished by activating an active hood system. For that purpose, the system has been mounted and tested on two different prototype cars and tested on private circuits using dummies.

Computer Vision and Image Understanding, 2010

During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system.

Advances in Mechanical Engineering, 2015

Pedestrians in the vehicle path are in danger of being hit, thus causing severe injury to pedestrians and vehicle occupants. Therefore, real-time pedestrian detection with the video of vehicle-mounted camera is of great significance to vehiclepedestrian collision warning and traffic safety of self-driving car. In this article, a real-time scheme was proposed based on integral channel features and graphics processing unit. The proposed method does not need to resize the input image. Moreover, the computationally expensive convolution of the detectors and the input image was converted into the dot product of two larger matrixes, which can be computed effectively using a graphics processing unit. The experiments showed that the proposed method could be employed to detect pedestrians in the video of car camera at 20 + frames per second with acceptable error rates. Thus, it can be applied in real-time detection tasks with the videos of car camera.