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IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
MSWiM '02 Proceedings of the 5th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems
Viewpoint Selection using Viewpoint Entropy
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Multiple-View-Based Tracking of Multiple Humans
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Online Multicamera Tracking with a Switching State-Space Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous localization, calibration, and tracking in an ad hoc sensor network
Proceedings of the 5th international conference on Information processing in sensor networks
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
Counting and localizing targets with a camera network
Counting and localizing targets with a camera network
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
On target tracking with binary proximity sensors
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Object tracking in the presence of occlusions via a camera network
Proceedings of the 6th international conference on Information processing in sensor networks
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Detection, classification, and collaborative tracking of multiple targets using video sensors
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Distributed Video Sensor Networks
Distributed Video Sensor Networks
Optimal placement and selection of camera network nodes for target localization
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Conditional Posterior Cramér–Rao Lower Bounds for Nonlinear Sequential Bayesian Estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras. Using simulations and preselected subsets of cameras, we investigate (i) the dependency of the tracker performance on the accuracy of the moving occluder priors, (ii) the trade-off between the number of cameras and the occluder prior accuracy required to achieve a prescribed tracker performance, and (iii) the importance of having occluder priors to the tracker performance as the number of occluders increases. We find that computing moving occluder priors may not be worthwhile, unless it can be obtained cheaply and to high accuracy. We also investigate the effect of dynamically selecting the subset of camera nodes used in tracking on the tracking performance. We show through simulations that a greedy selection algorithm performs close to the brute-force method and outperforms other heuristics, and the performance achieved by greedily selecting a small fraction of the cameras is close to that of using all the cameras.