CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
TRICam - An Embedded Platform for Remote Traffic Surveillance
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Error resilient image transport in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Autonomous multicamera tracking on embedded smart cameras
EURASIP Journal on Embedded Systems
A novel distributed privacy paradigm for visual sensor networks based on sharing dynamical systems
EURASIP Journal on Applied Signal Processing
Determining vision graphs for distributed camera networks using feature digests
EURASIP Journal on Applied Signal Processing
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
Multi-agent framework in visual sensor networks
EURASIP Journal on Applied Signal Processing
A scalable quorum-based location service in ad hoc and sensor networks
International Journal of Communication Networks and Distributed Systems
Incremental, scalable tracking of objects inter camera
Computer Vision and Image Understanding
Energy-efficient image transmission in sensor networks
International Journal of Sensor Networks
Low-complexity and energy efficient image compression scheme for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Localized Distance-Sensitive Service Discovery in Wireless Sensor and Actor Networks
IEEE Transactions on Computers
Challenging issues in visual sensor networks
IEEE Wireless Communications
Efficient Feature Distribution for Object Matching in Visual-Sensor Networks
IEEE Transactions on Circuits and Systems for Video Technology
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A recent publication by [SPKK] introduces a framework and set of rules by which object recognition can work on a visual sensor network. Extracted features of the detected object are flooded (with reduced dimensionality at each hop) in the network. The Sensor will match the corresponding feature of the new object with a locally stored one, and send the query on the backward link toward the original detector for matching. Based on their framework we introduce an algorithm which attempts to minimize the number of messages passed within the network when performing an image retrieval task. Extracted features are distributed along a row, while query matching progresses along a column. We compare our results to the algorithm proposed by [SPKK] and achieve fewer transmissions in the retrieval step, and avoid flooding in the pre-processing phase. We expand our algorithm by constructing an information mesh of multiple detections of the same object, to achieve matching with the nearest copy. We also propose a novel feature reduction method, by diving the image into k2 subimages, and extracting features in each subimage. This allows replacing histogram based features with a wide range of other options.