Mobile robot sonar for target localization and classification
International Journal of Robotics Research
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Space-Sweep Approach to True Multi-Image Matching
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Real-Time Target Localization and Tracking by N-Ocular Stereo
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Hydra: Multiple People Detection and Tracking Using Silhouettes
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coverage estimation for crowded targets in visual sensor networks
ACM Transactions on Sensor Networks (TOSN)
Collaborative localization in visual sensor networks
ACM Transactions on Sensor Networks (TOSN)
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Collaboration in visual sensor networks (VSNs) is essential not only to compensate for the processing, sensing, energy, and bandwidth limitations of each sensor node but also to improve the accuracy and robustness of the network. In this paper, we study target localization in VSNs, a challenging computer vision problem because of two unique features of cameras, including the extremely higher data rate and the directional sensing characteristics with limited field of view. Traditionally, the problem is solved by localizing the targets at the intersections of the back-projected 2D cones of each target. However, the existence of visual occlusion among targets would generate many false alarms. In this work, instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in the cone and generate the so-called certainty map of non-existence of targets. As a result, after fusing inputs from a set of sensor nodes, the unresolved regions on the certainty map would be the location of targets. This paper focuses on the design of a light-weight, energy-efficient, and robust solution where not only each camera node transmits a very limited amount of data but that a limited number of camera nodes is involved. We propose a dynamic itinerary for certainty map integration where the entire map is progressively clarified from sensor to sensor. When the confidence of the certainty map is satisfied, targets are localized at the remaining unresolved regions in the certainty map. Based on results obtained from both simulation and real experiments, the proposed progressive method shows effectiveness in detection accuracy as well as energy and bandwidth efficiency.