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Floating-Bagging-Adaboost ensemble for object detection using local shape-based features
Tang XS(唐旭晟); Shi ZL(史泽林); Li DQ(李德强); Ma L(马龙); Chen D(陈丹)
Department光电信息研究室
Conference Name2009 International Conference on Machine Learning and Cybernetics
Conference DateJuly 12-15, 2009
Conference PlaceBaoding , China
Author of SourceIEEE
Source Publication2009 International Conference on Machine Learning and Cybernetics
PublisherIEEE
Publication PlaceNew York
2009
Pages45-49
Indexed ByEI ; CPCI(ISTP)
EI Accession number20094612446241
WOS IDWOS:000281720400009
Contribution Rank1
Abstract

We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with floating search algorithm post optimization, for object detection that uses local shape-based feature. The feature use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to form an object detector. Floating search post optimization procedure is used to remove base classifiers which cause higher error rates. The resulting classifier consists of fewer base classifiers yet achieves better generalization performance. To demonstrate our method we trained a system to detect pedestrians in complex natural scenes. Experimental results show that our system can extremely rapidly detect objects with high detection rate. The learning techniques can be extended to detect other objects.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/7980
Collection光电信息技术研究室
Corresponding AuthorTang XS(唐旭晟)
AffiliationShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
Recommended Citation
GB/T 7714
Tang XS,Shi ZL,Li DQ,et al. Floating-Bagging-Adaboost ensemble for object detection using local shape-based features[C]//IEEE. New York:IEEE,2009:45-49.
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