Abnormal activity recognition in office based on T-transform", International Conference on Image Processing | |
Ying Wang; Kaiqi Huang![]() ![]() | |
2007 | |
会议名称 | IEEE International Conference on Image Processing, 2007 |
会议录名称 | IEEE International Conference on Image Processing, 2007 |
页码 | 341-344 |
会议日期 | 2007-09-01 |
会议地点 | San Antonio, Texas, USA |
摘要 | This paper introduces an abnormal activity recognition method based on a new feature descriptor for human silhouette. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The information that the initial silhouette carries is transformed in a compact way preserving important spatial information of the activities. Then a set of HMMs based on the features extracted by our method are trained to recognize abnormal activities. Experiments have proved the accuracy and efficiency of the proposed method, and the comparison with Fourier descriptor illustrates its robustness to disjoint shapes and shapes with holes. |
关键词 | Edge Detection feature Extraction hidden Markov Models |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12718 |
专题 | 模式识别实验室 |
通讯作者 | Kaiqi Huang |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Ying Wang,Kaiqi Huang,Tieniu Tan. Abnormal activity recognition in office based on T-transform", International Conference on Image Processing[C],2007:341-344. |
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