This paper presents a methodology for evaluating the performance of video surveillance tracking systems. We introduce a novel framework for performance evaluation using pseudo-synthetic video, which employs data captured online and stored in a surveillance database. Tracks are automatically selected from the surveillance database and then used to generate ground truthed video sequences with a controlled level of perceptual complexity that can be used to quantitatively characterise the quality of the tracking algorithms.
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Black et al. (2003) studied this question.