Abstract Based on the background image of a fixed scene, a four-step approach to count predestrains in video sequences is presented, and the estimation result of long-range crowds is improved compared with D.Conte’s solution in 2010 EURASIP Journal. Our primary contribution lies in non-maxima suppression clustering. The proposed density-based clustering approach applies different clustering standards to crowds at different distances from camera, hence it avoids overlarge clusters and ensuing problems. Experiments on PETS 2010 database show estimation result of long-range crowds is improved significantly, as an implicit result of smaller clusters from Non-maxima Suppression Clustering.
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2012, Vol. 25 
