NDLI: A study on occluded pedestrian detection based on block-based features and ensemble classifier

Content Provider	IEEE Xplore Digital Library
Author	Wu Bin Qu Shiru
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China (Wu Bin; Qu Shiru)
Abstract	When dealing with pedestrian detection under actual urban streets environment, pedestrian targets tend to be partially occluded in varying degrees because there are many vehicles and other transportation facilities, which affects the performance of the detection system. This paper presents a method for solving partial occlusions for pedestrian detection. It is based on the block-based feature and random subspace classifier to construct the ensemble classifiers. When the test results of holistic classifier is ambiguous, occlusion inference is conducted; if occlusion does exist, detection window will be classified by the ensemble classifier. Different pedestrian data sets are used: Daimler data set, TUD data set and real on-board pedestrian images. We test three different methods for both partially occluded and non-occluded data. Experimental results show that the method proposed in this paper performed better when dealing with partial occlusion situation and doesn't affect the detection performance for non-occluded targets.
Starting Page	4710
Ending Page	4715
File Size	2379614
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260367
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Training Accuracy pedestrian features Bismuth Feature extraction Cameras ensemble classifier occlusion processing Pedestrian detection Vehicles Testing
Content Type	Text
Resource Type	Article

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