关键词: 色情视频, 行为检测, 光流, 主成分分析（PCA）, 肤色检测

Abstract: Most traditional methods for recognizing pornographic videos are extending the pornographic image recognition methods directly without considering  “behavior”, which is the pivotal information that pornographic videos contain. The optical flow context histogram can describe the continuous behavior of moving objects. For the above-mentioned reasons, this paper proposes a new feature used to describe the behavior—the Optical Flow Context Histogram（OFCH）, and uses Principal Component Analysis（PCA） for feature dimension reduction. The PCA-OFCH features are used to train sensitive action classifier. The use of Bayes skin color prediction model based on histogram techniques can determine if it contains sufficient color information in video, in order to reduce the false alarm rate. Experimental results show that the proposed features based on PCA-OFCH with skin color detection can effectively identify pornographic video and normal video, which provides new ideas for pornographic video recognition.

Key words: pornographic video, behavior detection, optical flow, Principal Component Analysis（PCA）, skin color detection