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Ellis Validation of Blind Region Learning and Tracking
- Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation
, 2005
"... Multi view tracking systems enable an object’s identity to be preserved as it moves through a wide area surveillance network of cameras. One limitation of these systems is an inability to track objects between blind regions, i.e. parts of the scene that are not observable by the network of cameras. ..."
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Cited by 7 (1 self)
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Multi view tracking systems enable an object’s identity to be preserved as it moves through a wide area surveillance network of cameras. One limitation of these systems is an inability to track objects between blind regions, i.e. parts of the scene that are not observable by the network of cameras. Recent interest has been shown in blind region learning and tracking but not much work has been reported on the systematic performance evaluation of these algorithms. The main contribution of this paper is to define a set of novel techniques that can be employed to validate a camera topology model, and a blind region multi view tracking algorithm. 1.
Person tracking in Camera Networks Using Graph-Based Bayesian Inference
"... Abstract—In this paper, a probabilistic approach for tracking multiple persons through a network of distributed cameras is presented. The approach deals with the main problems associated with the tracking of persons through wide area networks-bridging large observation gaps between camera views and ..."
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Cited by 1 (0 self)
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Abstract—In this paper, a probabilistic approach for tracking multiple persons through a network of distributed cameras is presented. The approach deals with the main problems associated with the tracking of persons through wide area networks-bridging large observation gaps between camera views and reidentifying persons- by building on robust and view-invariant high-level features as well as a highly error-tolerant probabilistic filtering of person locations. The extraction quality and discriminative power of the proposed features is evaluated on realistic data including well-known and established benchmark datasets. A comparative performance analysis is then made to assess the accuracy of the probabilistic inter-camera tracking method given a number of different simulated and real quality levels of the underlying person detection and feature extraction components. The experiments are made using a simulated virtual environment involving multiple persons in an indoor surveillance scenario. I.
COMPOSED OF OVERLAPPING AND NON-OVERLAPPING CAMERAS
"... Multiple cameras have been used to improve the coverage and accuracy of visual surveillance systems. Nowadays, there are estimated 30 million surveillance cameras deployed in the United States. The large amount of video data generated by cameras necessitate automatic activity analysis, and automatic ..."
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Multiple cameras have been used to improve the coverage and accuracy of visual surveillance systems. Nowadays, there are estimated 30 million surveillance cameras deployed in the United States. The large amount of video data generated by cameras necessitate automatic activity analysis, and automatic object detection and tracking are essential steps before any activity/event analysis. Most work on automatic tracking of objects across multiple camera views has considered systems that rely on a back-end server to process video inputs from multiple cameras. In this dissertation, we propose distributed camera systems in peer-topeer communication. Each camera in the proposed systems performs object detection and tracking individually and only exchanges a small amount of data for consistent labeling.
Performance Analysis for Gait in Camera Networks
"... This paper deploys gait analysis for subject identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless gait analysis that does not require camera calibration and works with a wide range of directions of walking. These properties make the prop ..."
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This paper deploys gait analysis for subject identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless gait analysis that does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for gait identification in real surveillance scenarios where people and their behaviour need to be tracked across a set of cameras. Tests on 300 synthetic and real video sequences, with subjects walking freely along different walking directions, have been performed. Since the choice of the cameras ’ characteristics is a key-point for the development of a smart surveillance system, the performance of the proposed approach is measured with respect to different video properties: spatial resolution, frame-rate, data compression and image quality. The obtained results show that markerless gait analysis can be achieved without any knowledge of camera’s position and subject’s pose. The extracted gait parameters allow recognition of people walking from different views with a mean recognition rate of 92.2 % and confirm that gait can be effectively used for subjects ’ identification in a multi-camera surveillance scenario.
On the Graph Building Problem in Camera Networks
"... Abstract: In this paper, the problem of building a model of a sensor (camera) network from observations is considered. By model, we mean a graph where the nodes represent states that are observable and distinguishable by the sensor network and edges are the feasible transitions among these states: t ..."
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Abstract: In this paper, the problem of building a model of a sensor (camera) network from observations is considered. By model, we mean a graph where the nodes represent states that are observable and distinguishable by the sensor network and edges are the feasible transitions among these states: the edges are also weighted by the probability of transition from one state to another. Remarkably, since merely static observations are not sufficient to discern all states in the networked system, the dynamics of transition is also considered. In this respect, the proposed graph model appears falling into the class of hidden Markov models, where the discover of hidden states is made possible by exploiting the temporal evolution of the transitions and the implementation of a splitting procedure of previously identified graph nodes.
Path Recovery of a Disappearing Target in a Large Network of Cameras
"... A large network of cameras is necessary for covering large areas in surveillance applications. In such systems, gaps between the fields of view of different cameras are often unavoidable. We present a method for path recovery of a single target in such a network of cameras. The solution is robust, e ..."
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A large network of cameras is necessary for covering large areas in surveillance applications. In such systems, gaps between the fields of view of different cameras are often unavoidable. We present a method for path recovery of a single target in such a network of cameras. The solution is robust, efficient, and scalable with the network size. It is probably the first that can cope with hundreds of cameras and thousands of objects. The spatio-temporal topology of the network is assumed to be given. In addition, an algorithm for computing features that can be used to match the appearance of the object at different time steps is assumed to be available. Due to low video quality and limitations of the computed features, possi-ble confusion between the target and other objects can occur. The suggested method overcomes this challenge using a new modified particle filtering framework that produces at each time step a small set of candidate solutions represented by states. Each state con-sists of an object location and identity. Since invisible locations are explicitly modeled by states, the detection of disappearing and reappearing targets is inherent in the algorithm. A second phase re-covers the path using a dynamic programing algorithm on a layered graph that consists of the computed candidate states. A synthetic system with hundreds of cameras and thousands of moving objects is generated and used to demonstrate the efficiency and robustness of the method. The results depend, as expected, on the network topologies and the confusion level between objects. For challeng-ing cases our method obtained good results. 1.
Article The Video Collaborative Localization of a Miner’s Lamp Based on Wireless Multimedia Sensor Networks for Underground
, 2015
"... sensors ..."
Inference of Non-Overlapping Camera Network Topology
, 2011
"... This online database contains the full-text of PhD dissertations and Masters ’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license— ..."
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This online database contains the full-text of PhD dissertations and Masters ’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email (scholarship@uwindsor.ca) or by telephone at 519-253-3000ext. 3208.
Distributed Tracking in a Large-Scale Network of Smart Cameras
"... This paper describes a new distributed algorithm for track-ing in distributed camera networks. This algorithm oper-ates without a centralized server that collects all the mea-surements over the entire network. With the observations sent from its neighbors and the local probabilistic transition model ..."
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This paper describes a new distributed algorithm for track-ing in distributed camera networks. This algorithm oper-ates without a centralized server that collects all the mea-surements over the entire network. With the observations sent from its neighbors and the local probabilistic transition model, each camera independently estimates local paths in its neighborhood. The conflicts on locally estimated paths among cameras are resolved by a voting algorithm, and the agreed local paths are finally combined into global paths. Our experiments with simulated data demonstrate that the proposed distributed tracking algorithm is fast and scalable without degrading tracking accuracy.
