Feature compression: A framework for multi-view multi-person tracking in visual sensor networks
Serhan Cosar, Müjdat Çetin
Journal of Visual Communication and Image Representation, vol. 25, no. 5, pp. 864-873, July 2014
Visual sensor networks (VSNs) consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. In this paper, we present a framework for human track-ing in VSNs. The traditional approach of sending compressed images to a central node has certain disad-vantages such as decreasing the performance of further processing (i.e., tracking) because of low quality images. Instead, we propose a feature compression-based decentralized tracking framework that is better matched with the further inference goal of tracking. In our method, each camera performs feature extrac-tion and obtains likelihood functions. By transforming to an appropriate domain and taking only the sig-nificant coefficients, these likelihood functions are compressed and this new representation is sent to the fusion node. As a result, this allows us to reduce the communication in the network without significantly affecting the tracking performance. An appropriate domain is selected by performing a comparison between well-known transforms. We have applied our method for indoor people tracking and demon-strated the superiority of our system over the traditional approach and a decentralized approach that uses Kalman filter.