Paper Info
Title
Efficient human detection in crowded environment based on motion and appearance information
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.
Year
DOI
Venue
2013
10.1145/2499788.2499837
ICIMCS
Keywords
Field
DocType
template size,human template matching,detection accuracy,multi-sized template,efficient human detection method,appearance clue,appearance information,crowded environment,background subtraction,representative human template,matching probability,template matching
Template matching,Background subtraction,Computer vision,Edge maps,Point distribution model,Pattern recognition,Computer science,Filter (signal processing),Pixel,Artificial intelligence,Template,Linear regression
Conference
Citations 
PageRank 
References 
0
0.34
12
Authors
4
Name
Order
Citations
PageRank
Chuan-Shen Hu161.19
Min-Chun Hu217029.78
Wen-huang Cheng371573.78
Ja-ling Wu41569168.11