Strategic Approach for High Performance Object Tracking in a Network of Surveillance Cameras (original) (raw)
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Object Detection and Tracking in Distributed Surveillance Systems Using Multiple Cameras
Multisensor Fusion, 2002
The problem of detecting and tracking of non-rigid objects, as well as classifying their behavior in complex and cluttered environments, can take advantage from using multiple cameras to collect information to be fused either at the remote control center or at intermediate, local hubs. In this paper, multicamera configurations and processing schemes of surveillance systems will be explored by paying attention to their influence with respect to the choice of appropriate fusion methods and algorithms oriented to scene interpretation. In particular, signal representation levels will be described at which data fusion can occur in a system oriented to detect objects and their behavior. Moreover, two different categories of fusion methods will be identified together with their requisites and technical constraints: fusion methods necessary for cameras with partially overlapped field of views and fusion methods for cameras monitoring different local areas of a global environment. Multisensor fusion methods will be presented that are based either on computationally efficient search for correspondences in multisensor data or on global statistical modeling. Possible synergies of fusion algorithms with other components of a surveillance system will be highlighted: constraining effects on representation levels at which fusion may occur.will be shown, which are coming from the choice of transmission modalities over open multimedia networks as well as from the strategies of distribution of intelligence among multiple computational units. Examples will be presented of specific multisensor fusion systems used for detecting objects and their behaviors in surveillance systems operating in the transport field. Finally, possible trends in distributing fusion algorithms among available computational resources will be introduced with particular attention to home surveillance.
Human Detection, Tracking and Trajectory Extraction in a Surveillance Camera network
2013
This paper proposes human tracking and recognition method in a camera network. Human matching in a multi-camera surveillance system is a fundamental issue for increasing the accuracy of recognition in multiple views of cameras. In camera network, videos have different characteristics such as pose, scale and illumination. Therefore it is necessary to use a hybrid scheme of scale invariant feature transform to detection and recognition human's behaviors. The main focus of this paper is to analyze activities for tracking and recognition humans to extract trajectories. Extracting the trajectories help to detect abnormal behavior which may be occluded in single- camera surveillance. KEYWORDS: Camera network, Multi-camera surveillance, Human's behavior, Trajectories extraction. I. INTRODUCTION Tracking and behavior recognition are two fundamental tasks in video surveillance systems which are widely employed in commercial applications for purposes of statistics gathering and proces...
Study on Design and Implementation of Distributed Multiple Camera Surveillance and Tracking System
ITM Web of Conferences
This work proposes a system to successfully track, identify and tag target objects or individuals in a real time environment. Consider a sequence of cameras installed in an environment or a facility. Through these cameras user can track those intruders who are aware of how the surveillance system operates and are actively trying to avoid getting seen by the surveillance system. This system tracks the movement of any person’s movement and record and maintain that data throughout any facility in which such a solution is deployed. Movement logging of any individual person can also be done if it does not infringe his/her privacy. Design and analysis a distributed algorithm for the optimization of the recognition and mapping of the given subject/subjects on a User Interface. Finally, the performance and robustness of the distributed system is further analyzed through continuously training the algorithm or maybe real time demo.
IEEE Sensors Journal, 2015
Object detection and tracking are two fundamental tasks in multi-camera surveillances. This paper proposes a framework for achieving these tasks in a non-overlapping multiple camera network. A new object detection algorithm using mean shift (MS) segmentation is introduced and occluded objects are further separated with the help of depth information derived from stereo vision. The detected objects are then tracked by a new object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). It employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. When coupled with an improved MS tracker, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is obtained. Furthermore, a non-training-based object recognition algorithm is employed to support object tracking over non-overlapping network. Experimental results show that: 1) the proposed object detection algorithm yields improved segmentation results over conventional object detection methods, 2) the proposed tracking algorithm can successfully handle complex scenarios with good performance and low arithmetic complexity. Moreover, the performance of both non-training/ training-based object recognition algorithms can be improved by using our detection and tracking results as input.
Multiple cameras using real time object tracking for surveillance and security system
Emerging Trends in …, 2010
In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.
Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review
International Journal on Artificial Intelligence Tools
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 pa...
A Multi-Camera Visual Surveillance System for Tracking of Reoccurrences of People
2007 First ACM/IEEE International Conference on Distributed Smart Cameras, 2007
This paper describes a software system to track the reoccurrences of objects in multi-camera visual surveillance. Specifically, it is designed for after-event tracking of people to aid in a typical investigation of events occurring in a certain location at a certain time. This is a nontrivial problem because of several aspects that influence the appearance of scenes and people, such as changes in viewpoints, lighting conditions, shadow, occlusion, and weather conditions. Another challenge, which is the focus of this paper, is to integrate different required components into a complete working system, namely (i) motion detection, (ii) object classification, (iii) object modeling and matching, and (iv) interactive retrieval and visualization. We have designed and implemented a robust system consisting of state-of-the-art technologies in each component. We performed experiments with the system on a reallife dataset gathered from 12 street surveillance cameras over two hours in a city area. The experiments showed promising results in retrieving the reoccurrences of four target subjects.
Online and Real-Time Tracking in a Surveillance Scenario
ArXiv, 2021
This paper presents an approach for tracking in a surveillance scenario. Typical aspects for this scenario are a 24/7 operation with a static camera mounted above the height of a human with many objects or people. The Multiple Object Tracking Benchmark 20 (MOT20) reflects this scenario best. We can show that our approach is real-time capable on this benchmark and outperforms all other realtime capable approaches in HOTA, MOTA, and IDF1. We achieve this by contributing a fast Siamese network reformulated for linear runtime (instead of quadratic) to generate fingerprints from detections. Thus, it is possible to associate the detections to Kalman filters based on multiple tracking specific ratings: Cosine similarity of fingerprints, Intersection over Union, and pixel distance ratio in the image.