Online Adaptive Dictionary Learning and Weighted Sparse Coding for Abnormality Detection
文献类型：会议论文
| 作者 | Sheng Han; Ruiqing Fu; Suzhen Wang; Xinyu Wu |
| 出版日期 | 2013 |
| 会议名称 | 2013 20th IEEE International Conference on Image Processing, ICIP 2013 |
| 会议地点 | Melbourne, VIC, Australia |
| 英文摘要 | This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4606]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Sheng Han,Ruiqing Fu,Suzhen Wang,et al. Online Adaptive Dictionary Learning and Weighted Sparse Coding for Abnormality Detection[C]. 见:2013 20th IEEE International Conference on Image Processing, ICIP 2013. Melbourne, VIC, Australia. |
入库方式： OAI收割
来源：深圳先进技术研究院
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