Title :
Structured Local Edge Pattern Moment for pedestrian detection
Author :
Su, Song-Zhi ; Chen, Shu-Yuan ; Li, Shao-Zi ; Duh, Der-Jyh
Author_Institution :
Cognitive Sci. Dept., Xiamen Univ., Xiamen, China
Abstract :
Local feature based approaches have gotten great success in object detection and recognition in recent years. In this paper, a novel local based feature, Structured Local Edge Pattern Moment (SLEPm), is proposed for pedestrian detection in the sliding window framework. SLEPm encodes not only the statistical information but also the structure and spatial information of object for pedestrian detection. Linear Support Vector Machine (SVM) is used as a binary classifier to determine whether a sub-window contains pedestrian. Experimental results in INRIA pedestrian database show that performance of SLEPm is better than that of Histogram of Oriented Gradient (HOG).
Keywords :
feature extraction; object detection; pattern classification; support vector machines; INRIA pedestrian database; binary classifier; histogram of oriented gradient; linear support vector machine; local feature based approaches; object detection; pedestrian detection; sliding window framework; spatial information; statistical information; structure information; structured local edge pattern moment; Computer science; Face detection; Filters; Head; Image edge detection; Intelligent robots; Leg; Object detection; Support vector machine classification; Support vector machines; Linear SVM; Local Edge Pattern; Object Detection; Pedestrian Detection; Structured Local Edge Pattern Moment;
Conference_Titel :
Image Analysis and Signal Processing (IASP), 2010 International Conference on
Conference_Location :
Zhejiang
Print_ISBN :
978-1-4244-5554-6
Electronic_ISBN :
978-1-4244-5556-0
DOI :
10.1109/IASP.2010.5476054
