Distributed Processing of Visual Features in Wireless Sensor Networks

Abstract: As digital cameras are becoming both cheaper and more advanced, they are also becoming more common both as part of hand-held and consumer devices, and as dedicated surveillance devices. The still images and videos collected by these cameras can be used as input to computer vision algorithms for performing tracking, scene understanding, navigation, etc. The performance of such computer vision tasks can be improved by having multiple cameras observing the same events. However, large scale deployment of camera networks is difficult in areas without access to infrastructure for providing power and network connectivity. In this thesis we consider the use of a network of camera equipped sensor nodes as a cost efficient alternative to conventional camera networks. To overcome the computational limitations of the sensor nodes, we enhance the sensor network with dedicated processing nodes, and process images in parallel using multiple processing nodes.In the first part of the thesis, we formulate the minimization problem of the time required from image capture until the visual features are extracted from the image. The solution to the minimization problem is an allocation of sub-areas of a captured image to a subset of the processing nodes, which perform the feature extraction. We use the temporal correlation of the image contents to predict an approximation of the distribution of visual features in a captured image. Based on the approximate distribution, we compute an approximate solution to the minimization problem using linear programming. We show that the last value predictor gives a good trade-off between performance and computational complexity.In the second part of the thesis, we propose fully distributed algorithms for allocation of image sub-areas to the processing nodes in a multi-camera Visual Sensor Network. The algorithms differ in the amount of information available and in how allocation updates are applied. We provide analytical results on the existence of equilibrium allocations, and show that an equilibrium allocation may not be optimal. We show that fully distributed algorithms are most efficient when sensors make asynchronous changes to their allocations, and in topologies with less symmetry. However, with the addition of sparse coordination, both average and worst-case performance can be improved significantly.