Global and Local Training for Moving Object Classification in Surveillance-Oriented Scene
文献类型：会议论文
| 作者 | Xin Zhao ; Jianwei Ding; 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 image Motion Analysis learning (Artificial Intelligence |
| 页码 | 681-685 |
| 英文摘要 | This paper presents a new training framework for multi-class moving object classification in surveillance-oriented scene. In many practical multi-class classification tasks, the instances are close to each other in the input feature space when they have similar features. These instances may have different class labels. Since the moving objects may have various view and shape, the above phenomenon is common in multi-class moving object classification. In our framework, firstly the input feature space is divided into several local clusters. Then, global training and local training are carried out sequential with an efficient online learning based algorithm. The induced global classifier is used to assign candidate instances to the most reliable clusters. Meanwhile, the trained local classifiers within those clusters can determine which classes the candidate instances belong to. Our experimental results illustrate the effectiveness of our method for moving object classification in surveillance-oriented scene. |
| 会议录 | Pattern Recognition, 2011
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12694]  |
| 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,Jianwei Ding,Kaiqi Huang,et al. Global and Local Training for Moving Object Classification in Surveillance-Oriented Scene[C]. 见:. Beijing, China. 2011. |
入库方式： OAI收割
来源：自动化研究所
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