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Stereo vision-based pedestrian detection using dense disparity map-based detection and segmentation

Title
Stereo vision-based pedestrian detection using dense disparity map-based detection and segmentation
Authors
Lee, Chung-HeeKim, Dongyoung
DGIST Authors
Lee, Chung-Hee; Kim, Dongyoung
Issue Date
2015
Citation
6th International Conference on Graphic and Image Processing, ICGIP 2014, 9443
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
0277-786X
Abstract
In this paper, we propose a stereo vision-based pedestrian detection method using a dense disparity map-based detection and segmentation algorithm. To enhance a pedestrian detection performance, we use a dense disparity map extracted from a global stereo matching algorithm. First, we extract a road feature information from the dense disparity map, which is a decision basis of presence or absence of obstacles on the road. It is very important to extract the road feature from the disparity for detecting obstacles robustly regardless of external traffic situations. The obstacle detection is performed with the road feature information to detect only obstacles from entire image. In other words, pedestrian candidates including various upright objects are detected in the obstacle detection stage. Each obstacle area tends to include multiple objects. Thus, a disparity map-based segmentation is performed to separate the obstacle area into each obstacle accurately. And then, accurate pedestrian areas are extracted from segmented obstacle areas using road contact and pedestrian height information. This stage enables to reduce false alarms and to enhance computing speed. To recognize pedestrians, classifier is performed in each verified pedestrian candidate. Finally, we perform a verification stage to examine the recognized pedestrian in detail. Our algorithms are verified by conducting experiments using ETH database. © 2015 SPIE.
URI
http://hdl.handle.net/20.500.11750/3712
DOI
10.1117/12.2178695
Publisher
SPIE
Related Researcher
Files:
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Collection:
Intelligent Devices and Systems Research Group2. Conference Papers


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