Collaborative target tracking using multiple visual features in smart camera networks

Abstract

With the evolution and fusion of technologies from sensor networks and embedded cameras, smart camera networks are emerging as useful and powerful systems. Wireless networks, however, introduce new constraints of limited bandwidth, computation, and power. Existing camera network approaches for target tracking either utilize target handover mechanisms between cameras, or combine results from 2D trackers into 3D target state for continuous tracking. Such approaches suffer from the drawbacks associated with 2D tracking, such as scale selection, target rotation, and occlusion. In this paper, we present an approach for tracking multiple targets in 3D space using a wireless network of smart cameras. In our approach, we use multiview histograms in different feature-spaces to characterize targets in 3D space. We employ color and texture as the visual features to model targets. The visual features from each camera, along with the target models are used in a probabilistic tracker to estimate the target state. We demonstrate the effectiveness of our proposed tracker with results tracking people using a camera network deployed in a building.