Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker
文献类型：会议论文
| 作者 | Suiwu Zheng ; Linshan Liu; Hong Qiao
|
| 出版日期 | 2014 |
| 会议名称 | Neural Information Processing. 21st International Conference, ICONIP 2014 |
| 会议日期 | 3-6 Nov. 2014 |
| 会议地点 | Kuching, Malaysia |
| 关键词 | NONE |
| 通讯作者 | Suiwu Zheng |
| 英文摘要 | Robust object tracking in crowded and cluttered dynamic scenes is a very difficult task in robotic vision due to complex and changeable environment and similar features between the background and foreground. In this paper, a saliency feature extraction method is fused into mean-shift tracker to overcome above difficulties. First, a spatial-temporal saliency feature extraction method is proposed to suppress the interference of the complex background. Furthermore, we proposed a saliency evaluation method by fusing the top-down visual mechanism to enhance the tracking performance. Finally, the efficiency of the saliency features based mean-shift tracker is validated through experimental results and analysis. |
| 会议录 | Neural Information Processing. 21st International Conference, ICONIP 2014. Proceedings: LNCS 8834
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12864]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 推荐引用方式 GB/T 7714 | Suiwu Zheng,Linshan Liu,Hong Qiao. Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker[C]. 见:Neural Information Processing. 21st International Conference, ICONIP 2014. Kuching, Malaysia. 3-6 Nov. 2014. |
入库方式： OAI收割
来源：自动化研究所
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