Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance
文献类型：会议论文
| 作者 | Zhaoxiang Zhang ; Min Li; Kaiqi Huang; Tieniu Tan
|
| 出版日期 | 2008-12-08 |
| 会议日期 | 8-11 December 2008 |
| 会议地点 | Tampa, Florida, USA |
| 关键词 | Boosting Layout Surveillance Videos Object Detection Hidden Markov Models Fuses Noise Robustness Cameras Motion Detection |
| 页码 | 1-4 |
| 英文摘要 | We address the problem of automatic object classification for traffic scene surveillance, which is very challenging for the low resolution videos, large intra-class variations and real-time requirement. In this paper, we propose a new strategy for object classification by boosting different local feature descriptors in motion blobs. We not only evaluate the performance of each local feature descriptor, but also fuse these descriptors to achieve better performance. Numerous experiments are conducted and experimental results demonstrate the effectiveness and efficiency of our approach with robustness to noise and variance of view angles, lighting conditions and environments. |
| 会议录 | Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12714]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Zhaoxiang Zhang,Min Li,Kaiqi Huang,et al. Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance[C]. 见:. Tampa, Florida, USA. 8-11 December 2008. |
入库方式： OAI收割
来源：自动化研究所
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