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An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection

文献类型：期刊论文

作者Xu, Yanwu1,2; Cao, Xianbin1,3; Qiao, Hong4,5
刊名IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
出版日期2011-02-01
卷号41期号:1页码:107-117
关键词Efficient classification false-positive rate (FPR) pedestrian detection performance evaluation radial basis function (RBF) neural network.
英文摘要Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast.
WOS标题词Science & Technology ; Technology
类目[WOS]Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
研究领域[WOS]Automation & Control Systems ; Computer Science
关键词[WOS]NIGHT-VISION ; TRACKING ; ACCURACY ; PATTERNS
收录类别SCI
语种英语
WOS记录号WOS:000286388300009
源URL[http://ir.ia.ac.cn/handle/173211/3008]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位1.Univ Sci & Technol China, Hefei 230026, Peoples R China
2.Nanyang Technol Univ, Singapore 639798, Singapore
3.BeiHang Univ, Beijing 100083, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
5.Univ Manchester, Manchester M13 9PL, Lancs, England
推荐引用方式
GB/T 7714		 Xu, Yanwu,Cao, Xianbin,Qiao, Hong. An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2011,41(1):107-117.
APA Xu, Yanwu,Cao, Xianbin,&Qiao, Hong.(2011).An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,41(1),107-117.
MLA Xu, Yanwu,et al."An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 41.1(2011):107-117.

入库方式： OAI收割

来源：自动化研究所

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