-experts classifier on the unoccluded body parts. In experiments on extensive real-world data sets, with both partially occluded and non-occluded pedestrians, we obtain significant performance boosts over state-of-the-art approaches by up to a factor of four in reduction of false positives at constant detection rates

of pedestrians. We evaluate the accuracy of the proposed method using several video sequences, focusing in particular on the effect of parameters for optical flow clustering. We find that the proposed method improves the counting accuracy by up to 25 % as compared with a non-clustering method. We also report

occluded pedestrians viewed by a station-ary camera. The paper contains two novel contributions for the human detection task: 1. computationally efficient detection based on shape templates using contour integra-tion by means of integral images which are built by oriented string scans; (2) a non

hierarchical tree matching scheme and combined with an support vector machine classifier to perform human/non-human classifi-cation. We also formulate multiple occluded human detection using a Bayesian framework and optimize it through an iterative process. We evaluated the approach on several public

in partial ful2llment of the requirements for the degree of