A heuristic deformable pedestrian detection method

Authors:
Yongzhen Huang;Kaiqi Huang;Tieniu Tan
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Year:
2010

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Abstract

Pedestrian detection is an important application in computer vision. Currently, most pedestrian detection methods focus on learning one or multiple fixed models. These algorithms rely heavily on training data and do not perform well in handling various pedestrian deformations. To address this problem, we analyze the cause of pedestrian deformation and propose a method to adaptively describe the state of pedestrians' parts. This is valuable to resolve the pedestrian deformation problem. Experimental results on the INRIA human dataset and our pedestrian pose database demonstrate the effectiveness of our method.