A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Byzantine Generals Problem
ACM Transactions on Programming Languages and Systems (TOPLAS)
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
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
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Fault-tolerant target localization in sensor networks
EURASIP Journal on Wireless Communications and Networking
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 limitations of each sensor node but also to tolerate inaccurate information generated by faulty sensors in the network. Fault tolerance in VSNs is more challenging than in conventional scalar sensor networks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion. This paper focuses on the design of a collaborative target localization algorithm in VSNs that would not only accurately localize targets but also detect the faults in camera orientation, tolerate these errors and further correct them before they cascade. Targets are localized based on distributed camera nodes integrating the so-called certainty map generated at each node, that records the target non-existence information within the camera's field of view. Based on the locations of detected targets in the final certainty map, we then construct a generative image model in each camera that estimates the camera orientation, detect inaccuracies in camera orientations and correct them. Based on results obtained from both simulation and real experiments, we show that the proposed fault-tolerant method is effective in localization accuracy as well as fault detection and correction performance.