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A New Algorithm for Robust Pedestrian Tracking Based on Manifold Learning and Feature Selection
Wang, Min1; Qiao, Hong1; Zhang, Bo2,3
2011-12-01
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
Volume12Issue:4Pages:1195-1208
AbstractManifold learning has been a popular method in many areas such as classification and recognition. In this paper, we propose a novel algorithm for pedestrian tracking based on our previous work on manifold learning. A new kind of manifold subspace is introduced, in which the intrinsic features of the target's motion can be best preserved, and the dimensionality of feature is very low. In the proposed subspace, variations of continuous pedestrian postures can be represented well by these intrinsic features. This also validates our conjecture that the movement of pedestrians can be described by some intrinsic and low-dimensional features, which are significant for tracking. Although intrinsic features are useful for tracking, algorithms that directly apply intrinsic features could not guarantee stable performance due to the influence from a complicated background. To address this issue, a foreground extraction method is introduced to enhance tracking stability by selecting the most discriminative color features to automatically distinguish the foreground from the candidate image. This preprocessing stage is proven to promote the accuracy of low-dimensional feature representation in pedestrian tracking. The whole tracking procedure, particularly dimensionality reduction, is linear and fast without complicated calculations. The experimental results validate the effectiveness of our algorithm under challenging conditions, such as a complex background, various pedestrian postures, and even occlusion.
KeywordFeature extraction manifold learning tracking
DOI10.1109/TITS.2011.2148717
Language英语
Funding ProjectNational Natural Science Foundation (NNSF) of China[61033011] ; National Natural Science Foundation (NNSF) of China[60725310] ; National Natural Science Foundation (NNSF) of China[90820007] ; 863 Program of China[2007AA04Z228] ; 973 Program of China[2007CB311002]
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000297588500025
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/12417
Collection应用数学研究所
Affiliation1.Chinese Acad Sci, Lab Complex Syst & Intelligent Sci, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, State Key Lab Sci & Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Min,Qiao, Hong,Zhang, Bo. A New Algorithm for Robust Pedestrian Tracking Based on Manifold Learning and Feature Selection[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2011,12(4):1195-1208.
APA Wang, Min,Qiao, Hong,&Zhang, Bo.(2011).A New Algorithm for Robust Pedestrian Tracking Based on Manifold Learning and Feature Selection.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12(4),1195-1208.
MLA Wang, Min,et al."A New Algorithm for Robust Pedestrian Tracking Based on Manifold Learning and Feature Selection".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 12.4(2011):1195-1208.
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