Visual Sensor Nodes Research Articles

Feature-Based Image Comparison for Semantic Neighbor Selection in Resource-Constrained Visual Sensor Networks

Visual Sensor Networks (VSNs), formed by large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Sensor collaboration is essential to VSNs and normally performed among sensors having similar measurements. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. In this paper, we propose the concept of forming semantic neighbors, where collaboration is only performed among geographically-close nodes that capture similar images, thus requiring image comparison as a necessary step. To avoid large amount of data transfer, we propose feature-based image comparison as features provide more compact representation of the image. The paper studies several representative feature detectors and descriptors, in order to identify a suitable feature-based image comparison system for the resource-constrained VSN. We consider two sets of metrics from both the resource consumption and accuracy perspectives to evaluate various combinations of feature detectors and descriptors. Based on experimental results obtained from the Oxford dataset and the MSP dataset, we conclude that the combination of Harris detector and moment invariants presents the best balance between resource consumption and accuracy for semantic neighbor formation in VSNs.