Covert Video Classification By Codebook Growing Pattern
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2016)
摘要
Recent advances in visual data acquisition and Internet technologies make it convenient and popular to collect and share videos. These activities, however, also raise the issue of privacy invasion. One potential privacy threat is the unauthorized capture and/or sharing of covert videos, which are recorded without the awareness of the subject(s) in the video. Automatic classification of such videos can provide an important basis toward addressing relevant privacy issues. The task is very challenging due to the large intra-class variation and between-class similarity, since there is no limit in the content of a covert video and it may share very similar content with a regular video. The challenge brings troubles when applying existing content-based video analysis methods to covert video classification.In this paper, we propose a novel descriptor, codebook growing pattern (CGP), which is derived from latent Dirichlet allocation (LDA) over optical flows. Given an input video V, we first represent it with a sequence of histograms of optical flow (HOF). After that, these HOFs are fed into LDA to dynamically generate the codebook for V. The CGP descriptor is then defined as the growing codebook sizes in the LDA procedure. CGP fits naturally for covert video representation since (1) optical flows can capture the camera motion that characterizes the covert video acquisition, and (2) CGP by itself is insensitive to video content. To evaluate the proposed approach, we collected a large covert video dataset, the first such dataset to our knowledge, and tested the proposed method on the dataset. The results show clearly the effectiveness of the proposed approach in comparison with other state-of-the-art video classification algorithms.
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关键词
covert video classification,codebook growing pattern,CGP,privacy invasion,video automatic classification,intraclass variation,between-class similarity,content-based video analysis,latent Dirichlet allocation,LDA,histograms of optical flow sequence,HOF sequence,covert video representation,camera motion,covert video acquisition
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