Pedestrian detection in crowded scenes

Pao Yue-kong Library Electronic Theses Database

Pedestrian detection in crowded scenes

 

Author: Ma, Ming-kai
Title: Pedestrian detection in crowded scenes
Degree: M.Sc.
Year: 2010
Subject: Hong Kong Polytechnic University -- Dissertations
Pedestrian traffic flow
Pedestrians -- Mathematical models.
Image analysis -- Mathematical models
Department: Dept. of Electronic and Information Engineering
Pages: [v], 51 leaves : ill. ; 31 cm.
InnoPac Record: http://library.polyu.edu.hk/record=b2356812
URI: http://theses.lib.polyu.edu.hk/handle/200/5874
Abstract: The challenge in pedestrian detection lies on the wide variations in human appearance and poses that can occur in a sampled scene. Successful detection in crowded scenes with overlapping pedestrians and background clutter requires classifiers with sufficient complexity. This thesis will study an efficient feature for representing pedestrians, namely the histogram of oriented gradients (HOG), and will demonstrate a dense-scan multi-resolution pedestrian detector with improvements in terms of computation speed and accuracy. A multi-resolution cascade consisting of two layers of HOG detectors with different block sizes is developed, The first layer is a HOG classifier with a larger block size that can efficiently reject simple non-pedestrian samples. The second layer of the cascade consists of a HOG classifier with a smaller block size to increase detection accuracy. To further reduce the computation time while minimizing the memory constraints, nonnalized blocks are buffered to reduce the redundancy between the successive detection windows. Finally, a scheme will be proposed to reduce the computationally intensive square foot and inverse tan calculations required on the gradients in each pixel through using lookup tables. Experimental results show that the proposed method can reduce the computation time per frame by approximately 9.4%, and maintaining the same detection accuracy.

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