Abstract
In this paper an algorithm for multicamera pedestrian detection is proposed. The first stage of this work is based on the probabilistic occupancy map framework, in which the ground plane is discretized into a grid and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in all camera views. In the second stage, where we borrowed the idea from the Quine-McCluskey method for logic function minimization, essential candidates are initially identified, each of which covers at least a significant part of the foreground that is not covered by the other candidates. Then non-essential candidates are selected to cover the remaining foregrounds by following an iterative process, which alternates between merging redundant candidates and finding emerging essential candidates. Experiments on benchmark video datasets have demonstrated the improved performance of this algorithm in comparison with some benchmark non-deep or deep multicamera/monocular algorithms for pedestrian detection.
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