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	Combination features and models for human detection
	Jiang, Yunsheng ; Ma, Jinwen
	2015
英文摘要	This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of feature/model may work well for some types of human bodies, but not for all the types. To tackle this difficult problem, we combine certain complementary features/models together with effective organization/fusion methods. Specifically, the HOG features, color features and bar-shape features are combined together with a cell-based histogram structure to form the so-called HOG-III features. Moreover, the detections from different models are fused together with the new proposed weighted-NMS algorithm, which enhances the probable 'true' activations as well as suppresses the overlapped detections. The experiments on PASCAL VOC datasets demonstrate that, both the HOG-III features and the weighted-NMS fusion algorithm are effective (obvious improvement for detection performance) and efficient (relatively less computation cost): When applied to human detection task with the Grammar model and Poselet model, they can boost the detection performance significantly; Also, when extended to detection of the whole VOC 20 object categories with the deformable part-based model and deep CNN-based model, they still show competitive improvements. ? 2015 IEEE.; EI; 240-248; 07-12-June-2015
语种	英语
出处	EI
出版者	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/436563]
专题	数学科学学院
推荐引用方式 GB/T 7714	Jiang, Yunsheng,Ma, Jinwen. Combination features and models for human detection. 2015-01-01.

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