关键词: 运动模式, 时空描述符, 视觉单词, 稀疏主题模型, 重构精度

Abstract:

A novel unsupervised method based on motion pattern analysis is proposed to detect abnormal activity in video flow. In order to effectively describe the object information in the video scene, spatial-temporal descriptors are extracted for each foreground pixel combined with block region information, and the video is represented by visual words which are generated through the bag-of-words model. Then, the sparse topic model is proposed to obtain the motion topics in video. Through this model, the motion patterns can be discovered and used to encode each video document. Finally, through the analysis of the reconstruction accuracy and the motion pattern composition, the test video clip can be determined whether it contains abnormal activity or not. Experiments are conducted on QMUL Junction dataset. The results demonstrate the superior efficiency of the proposed method.

Key words: motion pattern, spatial-temporal descriptors, visual words, sparse topic model, reconstruction accuracy