Decentralized Tracking in Sensor Networks with Varying Coverage

Abstract

The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

The Dark Side of Decentralized Target Tracking : Unknown Correlations and Communication Constraints

“…Hence, these methods are able to utilize information more efficiently, but at the cost of only providing conservative results, given that certain assumptions are fulfilled. ICI and LE are evaluated in practical scenarios in [131,132]. Theoretical properties are studied in [128,129,159].…”

Consistent Distributed Track Fusion Under Communication Constraints

“…The inverse covariance intersection (ICI, [15,16]), is a relatively new method, guaranteeing consistency under relatively mild assumptions. The effectiveness of these methods have previously been studied, see, e.g., [17]. A geometrical approach, using Minkowski sums, for deriving an upper bound on the covariance of fused estimate is studied in [18].…”