Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges

Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed

PETS 2017: dataset and challenge

This paper indicates the dataset and challenges evaluated under PETS2017. In this edition PETS continues the evaluation theme of on-board surveillance systems for protection of mobile critical assets as set in PETS 2016. The datasets include (1) the ARENA Dataset; an RGB camera dataset, as used for PETS2014 to PETS 2016, which addresses protection of trucks; and (2) the IPATCH Dataset; a multi sensor dataset, as used in PETS2016, addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. The datasets allow for performance evaluation of tracking in low-density scenarios and detection of various surveillance events ranging from innocuous abnormalities to dangerous and criminal situations. Training data for tracking algorithms is released with the dataset; tracking data is also available for authors addressing only surveillance event detection challenges but not working on tracking

Features-based moving objects tracking for smart video surveillances: A review

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance

Human Detection and Tracking for Video Surveillance A Cognitive Science Approach

With crimes on the rise all around the world, video surveillance is becoming more important day by day. Due to the lack of human resources to monitor this increasing number of cameras manually new computer vision algorithms to perform lower and higher level tasks are being developed. We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients the theory of Visual Saliency and the saliency prediction model Deep Multi Level Network to detect human beings in video sequences. Furthermore we implemented the k Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. We achieved a detection precision of 83.11% and a recall of 41.27%. We obtained these results 76.866 times faster than classification on normal images.Comment: ICCV 2017 Venice, Italy Pages 5 Figures

Audio-Video Event Recognition System For Public Transport Security

International audienceThis paper presents an audio-video surveillance system for the automatic surveillance in public transport vehicle. The system comprises six modules including in particular three novel ones: (i) Face Detection and Tracking, (ii) Audio Event Detection and (iii) Audio-Video Scenario Recognition. The Face Detection and Tracking module is responsible for detecting and tracking faces of people in front of cameras. The Audio Event Detection module detects abnormal audio events which are precursor for detecting scenarios which have been predefined by end-users. The Audio-Video Scenario Recognition module performs high level interpretation of the observed objects by combining audio and video events based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined audio, video and audio-video events specified using an audio-video event ontology

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

Development Of Vehicle Tracking And Counting System From Traffic Surveillance Video

A vehicle counting and tracking system automatically detects and classifies vehicles from traffic surveillance video sequences. The system are used to replace manual labor to collect vehicles data for various application such as transportation planning and road safety evaluation. The existing Vehicle Detection and Classification System does not have tracking and counting module implemented. Tracking is required to enable automatic vehicle count. The objective of this project is to develop and implement tracking and counting feature into the existing vehicle detection and classification system, assess the vehicle detection and classification with tracking and counting feature system performances, and select the optimal parameters for tracking and counting module. Visual Background Extractor (ViBE) is used to extract the vehicles (foreground) from the traffic surveillance video sequences. Simple tracking and counting algorithm is used to track and count the detected vehicle. Histogram of Oriented Gradient (HOG) is used to extract features from the detected vehicle. Multi-class Support Vector Machine (SVM) is used to classify the detected vehicle into four classes, which are motorcycle, car, lorry, and non-vehicle. The system is evaluated using two video sequences which are 670 seconds long with total of 20100 frames. The overall system performance achieves 78.19 % and 88.14% for vehicle detection and classification, respectively

Human Detection and Tracking Using Hog Feature and Particle Filter

Video surveillance system has recently attracted much attention in various fields for monitoring and ensuring security. One of its promising applications is in crowd control to maintain the general security in public places. However, the problem of video surveillance systems is the required continuous manual monitoring especially for crime deterrence. In order to assist the security monitoring the live surveillance systems, intelligent target detection and tracking techniques can send a warning signal to the monitoring officers automatically. Towards this end, in this paper, we propose an innovative method to detect and track a target person in a crowded area using the individual’s features. In the proposed method, to realize automatic detection and tracking we combine Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The HOG feature is applied for the description of contour detection for the person, while the particle filter is used for tracking the targets using skin and clothes color based features. We have developed the evaluation system implementing our proposed method. From the experimental results, we have achieved high accuracy detection rate and tracked the specific target precisely