A comparison study on kernel based online learning for moving object classification
文献类型：会议论文
| 作者 | Xin Zhao ; Kaiqi Huang; Tieniu Tan
|
| 出版日期 | 2011 |
| 会议日期 | 2011 |
| 会议地点 | Beijing, China |
| 关键词 | Image Classification learning (Artificial Intelligence motion Estimation |
| 页码 | 17-20 |
| 英文摘要 | Most visual surveillance and video understanding systems require knowledge of categories of objects in the scene. One of the key challenges is to be able to classify any object in a real-time procedure in spite of changes in the scene over time and the varying appearance or shape of object. In this paper, we explore the applications of kernel based online learning methods in dealing with the above problems. We evaluate the performance of recently developed kernel based online algorithms combined with the state-of-the-art local shape feature descriptor. We perform the experimental evaluation on our dataset. The experimental results demonstrate that the online algorithms can be highly accurate to the problem of moving object classification. |
| 会议录 | Conference on Intelligent Visual Surveillance, 2011
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12699]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Kaiqi Huang |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Xin Zhao,Kaiqi Huang,Tieniu Tan. A comparison study on kernel based online learning for moving object classification[C]. 见:. Beijing, China. 2011. |
入库方式： OAI收割
来源：自动化研究所
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