Incremental, scalable tracking of objects inter camera

This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated cameras with non overlapping fields of view. The approach relies on the three cues of colour, relative size and movement between cameras to describe the relationship of objects between cameras. This relationship weights the observation likelihood for correlating or tracking objects between cameras. Any individual cue alone has poor performance, but when fused together, a large boost in accuracy is gained. Unlike previous work, this paper uses an incremental technique to learning. The three cues are learnt in parallel and then fused together to track objects across the spatially separated cameras. The colour appearance cue is incrementally calibrated through transformation matrices, while probabilistic links, modelling an object's bounding box, between cameras represent the objects relative size. Probabilistic region links between entry and exit areas on cameras provide the cue of movement. The approach needs no pre colour or environment calibration and does not use batch processing. It works completely unsupervised, and is able to become more accurate over time as new evidence is accumulated.