CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Learning semantic motion patterns for dynamic scenes by improved sparse topical coding
Fu, Wei; Wang, Jinqiao; Li, Zechao; Lu, Hanqing; Ma, Songde
2012
Conference NameIEEE International Conference on Multimedia and Expo
Source PublicationIEEE International Conference on Multimedia and Expo (ICME)
Pages296-301
Conference Date2012
Conference PlaceMelbourne, Australia
AbstractWith 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.
KeywordLearning Semantic Motion Patterns Dynamic Scenes Improved Sparse Topical Coding
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4674
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
Recommended Citation
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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