A Generalized Kalman Consensus Filter for wide-area video networks

Distributed analysis of video captured by a large network of cameras has received significant attention lately. Tracking moving targets is one of the most fundamental tasks in this regard and the well-known Kalman Consensus Filter (KCF) has been applied to this problem. However, existing solutions do not consider the specific characteristics of video sensor networks, which are necessary for robustness across various application scenarios. Cameras are directional sensors with limited sensing range (field-of-view), and thus, targets are often not observed by many of the cameras. The network may also be spread over a wide area, preventing direct communication between all of the cameras. This limited field-of-view, combined with sparse communication and coverage topologies, motivates us to propose modifications to the traditional KCF framework. Specifically, we consider the covariance matrices of the state estimates of the neighbors and compute a weighted average consensus estimate at each node. Also, the update at each node is computed in two steps, first towards the weighted consensus estimate and then towards the final Kalman measurement update. This leads us to propose a Generalized KCF herein. Experimental results clearly show the advantage of the GKCF compared to the KCF in the considered application scenario.