Pedestrian Detection Based on Clustered Poselet Models and Hierarchical AND-OR Grammar
Li, Bo1; Chen, Yaobin2,3; Wang, Fei-Yue1
AbstractIn this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the AND-OR graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments.
KeywordAnd-or Graph Clustered Poselet Computer Vision Pedestrian Detection
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaEngineering ; Telecommunications ; Transportation
WOS SubjectEngineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS IDWOS:000353111900015
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Indiana Univ Purdue Univ, Dept Elect & Comp Engn, Indianapolis, IN 46202 USA
3.Indiana Univ Purdue Univ, Transportat Act Safety Inst, Indianapolis, IN 46202 USA
Recommended Citation
GB/T 7714
Li, Bo,Chen, Yaobin,Wang, Fei-Yue. Pedestrian Detection Based on Clustered Poselet Models and Hierarchical AND-OR Grammar[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2015,64(4):1435-1444.
APA Li, Bo,Chen, Yaobin,&Wang, Fei-Yue.(2015).Pedestrian Detection Based on Clustered Poselet Models and Hierarchical AND-OR Grammar.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,64(4),1435-1444.
MLA Li, Bo,et al."Pedestrian Detection Based on Clustered Poselet Models and Hierarchical AND-OR Grammar".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 64.4(2015):1435-1444.
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