DocumentCode :
3266930
Title :
Pedestrian detection using hybrid statistical feature
Author :
Wu, Qiang ; Du, Chunhua ; Yang, Jie ; He, Xiangjian ; Chen, Yan
Author_Institution :
Sch. of Comput. & Commun., Univ. of Technol., Sydney, NSW
fYear :
2008
fDate :
8-10 Oct. 2008
Firstpage :
101
Lastpage :
106
Abstract :
A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA gait database and the additional non-human objects data.
Keywords :
feature extraction; image sequences; object detection; statistical analysis; CASIA gait database; gait energy image; human detection methods; hybrid statistical feature; nonhuman objects data; pedestrian detection; walking people detection; Assembly; Australia; Detectors; Humans; Layout; Legged locomotion; Motion detection; Spatial databases; Support vector machines; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Signal Processing, 2008 IEEE 10th Workshop on
Conference_Location :
Cairns, Qld
Print_ISBN :
978-1-4244-2294-4
Electronic_ISBN :
978-1-4244-2295-1
Type :
conf
DOI :
10.1109/MMSP.2008.4665056
Filename :
4665056
Link To Document :
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