Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource-constrained visual sensor network (VSN). Our results show high variability in the density, the spatial distribution, and the octave layer distribution of the interest points. The high variability implies that balancing the processing load among the sensor nodes is a very challenging task, and obtaining a priori information is essential, e.g., through prediction. Our results show that if a priori information is available about the images, then Top-interest point selection, limited, octave-based processing at the camera node, together with area-based interest point detection and extraction at the processing nodes, can balance the processing load and limit the transmission cost in the network. Complete interest point detection at the camera node with optimized descriptor extraction delegation to the processing nodes in turn can further decrease the transmission load and allow a better balance of the processing load among the network nodes.