Pedestrian segmentation using deformable triangulation and kernel density estimation

Jun-Wei Hsieh*, Sin Yu Chen, Chi Hung Chuang, Yung Sheng Chen, Zhong Yi Guo, Kuo Chin Fan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This paper proposes a novel kernel-based and technique to segment pedestrians from a single image. An important concept introduced in this paper is "detection before segmentation" for extracting pedestrians' boundaries more precisely no matter what cameras (mobile, PTZ, or stationary) are used or how does the background include various lighting changes. First of all, the Adaboost-based detector is trained for detecting all possible pedestrians from still images. Then, we adopt the Watershed algorithm to over-segment each frame as a rough segmentation. Since two homogenous regions will still connect together, a triangulation-based scheme is then used to divide them into different tinier regions using their edge features. Then, we propose a novel kernel density analysis to estimate the probability of each tinier region to be foreground or background. With the kernel modeling, an optimal segmentation of pedestrian can be found by maximizing a posteriori probability for maintaining the visual and spatial consistencies between each segmented regions. Then, each desired pedestrian can be more accurately extracted for content analysis even though it is occluded with other objects or captured by a mobile camera. Experimental results have shown the effectiveness and superiority of the proposed method in pedestrian segmentation.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Pages3270-3274
Number of pages5
DOIs
StatePublished - 10 Nov 2009
Event2009 International Conference on Machine Learning and Cybernetics - Baoding, China
Duration: 12 Jul 200915 Jul 2009

Publication series

NameProceedings of the 2009 International Conference on Machine Learning and Cybernetics
Volume6

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
CountryChina
CityBaoding
Period12/07/0915/07/09

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