Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30965
Title: Multi-instance local exemplar comparisons for pedestrian detection
Authors: Sun, C
Zhao, S
Hu, J
Lam, KM 
Keywords: Exemplar
Cascade
Multi-instance
Similarity
Template matching
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), 12-15 August 2012, Hong Kong, p. 223-227 How to cite?
Abstract: We propose to use the partial similarity between a sample and a number of exemplars as the image features for visual object detection. Define a part of the object as a sub-window inside the object bounding box, for each part of the object, a codebook of local appearance templates is learned. By using multiple templates for each part, and allowing the template to be compared with a bag of part instances in the neighborhood of the canonical location, the deformable and multi-aspect properties can be captured. A linear classifier is learned with feature selection, selecting a subset of the templates. To improve the efficiency of the detector, a rejection cascade is built by calibrating the linear classifier; the rejection cascade makes decisions using partial scores. Experimental results show that our method substantially improves the performance for human detection.
URI: http://hdl.handle.net/10397/30965
ISBN: 978-1-4673-2192-1
DOI: 10.1109/ICSPCC.2012.6335624
Appears in Collections:Conference Paper

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