NDLI: Efficient human detection in crowded environment based on motion and appearance information

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Efficient human detection in crowded environment based on motion and appearance information

Content Provider	ACM Digital Library
Author	Wu, Ja-Ling Cheng, Wen-Huang Hu, Chuan-Shen Hu, Min-Chun
Abstract	Detecting human in crowded environment is profitable but challenging in video surveillance. We propose an efficient human detection method by combining both motion and appearance clues. Moving pixels are first extracted by background subtraction, and then a filtering step is used to narrow the range for human template matching. We utilize integral images to fast generate shape information from edge maps of each frame and define the matching probability to be capable of detecting both full-body and partial-body. Representative human templates are constructed by sparse contours on the basis of the point distribution model (PDM). Moreover, linear regression analysis is also applied to adaptively adjust the template sizes. With the aid of the proposed foreground ratio filtering and the multi-sized template matching techniques, our method not only can efficiently detect human in a crowded environment but also largely enhance the detection accuracy.
Starting Page	97
Ending Page	100
Page Count	4
File Format	PDF
ISBN	9781450322522
DOI	10.1145/2499788.2499837
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sparse human contour Human detection Template matching
Content Type	Text
Resource Type	Article

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