3D target tracking in distributed smart camera networks with in-network aggregation

With the technology advancements in wireless sensor networks and embedded cameras, distributed smart camera networks are emerging for surveillance applications. Wireless networks, however, introduce bandwidth constraints that need to be considered. Existing approaches for target tracking typically utilize target handover mechanisms between cameras or combine results from 2D trackers into 3D target estimation. Such approaches suffer from scale selection, target rotation, and occlusion, drawbacks associated with 2D tracking. This paper presents an approach for tracking multiple targets in 3D space using a network of smart cameras. The approach employs multi-view histograms to characterize targets in 3D space using color and texture as the visual features. The visual features from each camera, along with the target models are used in a probabilistic tracker to estimate the target state. One of the main innovations in the proposed tracker is in-network aggregation in order to reduce communication cost. The effectiveness of the proposed approach is demonstrates using a camera network deployed in a building.