标题:An occlusion-robust feature selection framework in pedestrian detection
作者:Guo, Zhixin ;Liao, Wenzhi ;Xiao, Yifan ;Veelaert, Peter ;Philips, Wilfried
作者机构:[Guo, Zhixin ;Liao, Wenzhi ;Veelaert, Peter ;Philips, Wilfried ] Department of Telecommunications and Information Processing, Ghent University-Interun 更多
通讯作者:Guo, Zhixin
来源:Sensors (Switzerland)
出版年:2018
卷:18
期:7
DOI:10.3390/s18072272
摘要:Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned model. Current solutions typically exploit dimension reduction techniques. In this paper, we propose a simple but effective feature selection framework for pedestrian detection. Moreover, we introduce occluded pedestrian samples into the training process and combine it with a new feature selection criterion, which enables improved performances for occlusion handling problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our method over the state-of-art methods, especially for the occluded pedestrians.
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
收录类别:EI
资源类型:期刊论文
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