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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorMa, Ming-kai-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titlePedestrian detection in crowded scenesen_US
dcterms.abstractThe 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.en_US
dcterms.extent[v], 51 leaves : ill. ; 31 cm.en_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.LCSHPedestrian traffic flowen_US
dcterms.LCSHPedestrians -- Mathematical models.en_US
dcterms.LCSHImage analysis -- Mathematical modelsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/5874