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User-assisted visual search and tracking across distributed multi-camera networks

Human CCTV operators face several challenges in their task which can lead to missed events, people or associations, including: (a) data overload in large distributed multi-camera environments; (b) short attention span; (c) limited knowledge of what to look for; and (d) lack of access to non-visual contextual intelligence to aid search. Developing a system to aid human operators and alleviate such burdens requires addressing the problem of automatic re-identification of people across disjoint camera views, a matching task made difficult by factors such as lighting, viewpoint and pose changes and for which absolute scoring approaches are not best suited. Accordingly, we describe a distributed multi-camera tracking (MCT) system to visually aid human operators in associating people and objects effectively over multiple disjoint camera views in a large public space. The system comprises three key novel components: (1) relative measures of ranking rather than absolute scoring to learn the best features for matching; (2) multi-camera behaviour profiling as higher-level knowledge to reduce the search space and increase the chance of finding correct matches; and (3) human-assisted data mining to interactively guide search and in the process recover missing detections and discover previously unknown associations. We provide an extensive evaluation of the greater effectiveness of the system as compared to existing approaches on industry-standard i-LIDS multi-camera data.