Cooperative image analysis in visual sensor networks

This work addresses the problem of enabling resource-constrained sensor nodes to perform visual analysis tasks. The focus is on visual analysis tasks that require the extraction of local visual features, which form a succinct and distinctive representation of the visual content of still images or videos. The extracted features are then matched against a feature data set to support applications such as object recognition, face recognition and image retrieval. Motivated by the fact that the processing burden imposed by common algorithms for feature extraction may be prohibitive for a single, resource-constrained sensor node, this paper proposes cooperative schemes to minimize the processing time of the feature extraction algorithms by offloading the visual processing task to neighboring sensor nodes. The optimal offloading strategy is formally characterized under different networking and communication paradigms. The performance of the proposed offloading schemes is evaluated using simulations and is validated through experiments carried out on a real wireless sensor network testbed. The results show that the proposed offloading schemes allow to reduce the feature extraction time up to a factor of 3 in the reference scenario.