Learning semantic motion patterns for dynamic scenes by improved sparse topical coding

	Learning semantic motion patterns for dynamic scenes by improved sparse topical coding
	Fu, Wei; Wang, Jinqiao; Li, Zechao; Lu, Hanqing; Ma, Songde
	2012
会议名称	IEEE International Conference on Multimedia and Expo
会议录名称	IEEE International Conference on Multimedia and Expo (ICME)
页码	296-301
会议日期	2012
会议地点	Melbourne, Australia
摘要	With the proliferation of cameras in public areas, it becomes increasingly desirable to develop fully automated surveillance and monitoring systems. In this paper, we propose a novel unsupervised approach to automatically explore motion patterns occurring in dynamic scenes under an improved sparse topical coding (STC) framework. Given an input video with a fixed camera, we first segment the whole video into a sequence of clips (documents) without overlapping. Optical flow features are extracted from each pair of consecutive frames, and quantized into discrete visual words. Then the video is represented by a word-document hierarchical topic model through a generative process. Finally, an improved sparse topical coding approach is proposed for model learning. The semantic motion patterns (latent topics) are learned automatically and each video clip is represented as a weighted summation of these patterns with only a few nonzero coefficients. The proposed approach is purely data-driven and scene independent (not an object-class specific), which make it suitable for very large range of scenarios. Experiments demonstrate that our approach outperforms the state-of-theart technologies in dynamic scene analysis.
关键词	Learning Semantic Motion Patterns Dynamic Scenes Improved Sparse Topical Coding
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4674
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
推荐引用方式 GB/T 7714	Fu, Wei,Wang, Jinqiao,Li, Zechao,et al. Learning semantic motion patterns for dynamic scenes by improved sparse topical coding[C],2012:296-301.

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