COMBINING SPARSE APPEARANCE FEATURES AND DENSE MOTION FEATURES VIA RANDOM FOREST FOR ACTION DETECTION

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	COMBINING SPARSE APPEARANCE FEATURES AND DENSE MOTION FEATURES VIA RANDOM FOREST FOR ACTION DETECTION
	Yang, Shuang; Yuan, Chunfeng; Wang, Haoran; Hu, Weiming; weiming hu
	2013
会议名称	2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
会议录名称	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
页码	2415-2419
会议日期	2013
会议地点	Canada
摘要	This paper presents a new method to detect human actions in video by combining sparse appearance features and dense motion features in the unified random forest framework. We compute sparse appearance features to capture the main appearance changes and dense motion features to capture the tiny motion changes in the video. We take advantage of the randomization of channel selection in random trees to combine these two complementary types of features. In addition, linear classification is applied to grow each tree with high efficiency. Each leaf in these trees stores the class distribution and location information of the training samples and action detection for the test video is accomplished by Hough voting of the leaves in each tree. Experimental results demonstrate that our method achieves the state-of-the-art performance on two datasets.
关键词	无
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4542
专题	多模态人工智能系统全国重点实验室_视频内容安全
通讯作者	weiming hu
推荐引用方式 GB/T 7714	Yang, Shuang,Yuan, Chunfeng,Wang, Haoran,et al. COMBINING SPARSE APPEARANCE FEATURES AND DENSE MOTION FEATURES VIA RANDOM FOREST FOR ACTION DETECTION[C],2013:2415-2419.

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