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Efficient Vehicle Detection and Orientation Estimation by Confusing Subsets Categorization
Li FM(李非墨); Lan XS(兰晓松); Li SX(李书晓); Zhu CF(朱承飞); Chang HX(常红星)
2017-05
Conference Name2016 2nd IEEE International Conference on Computer and Communications
Conference Date2016-12
Conference Place中国四川成都
AbstractAerial traffic surveillance requires algorithms that can accurately predict the locations and orientations of hundreds of vehicles in a large high resolution aerial image within seconds. Under this constraint, the classical cascaded detection framework based on boosting algorithms still remains an optimal choice. These methods, however, usually use many binary classifiers to enhance the localization performance resistant to orientation variances, which is not effective in distinguishing confusing orientations and subsets. This paper categorizes these confusing subsets automatically by analyzing the correlations between specific orientation angles and location deviations at local detection window regions, makes robust predictions on them by N-nary multi-class classifiers. This helps to reduce the required number of classifiers to less than half and improve both localization and orientation estimation accuracies, making it potential for additional speed optimization.
KeywordHigh Resolution Aerial Image Vehicle Detection Orientation Estimation
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14580
Collection综合信息系统研究中心
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Li FM,Lan XS,Li SX,et al. Efficient Vehicle Detection and Orientation Estimation by Confusing Subsets Categorization[C],2017.
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