Tracking blurred object with data-driven tracker
文献类型：会议论文
| 作者 | Jianwei Ding; Kaiqi Huang ; Tieniu Tan
|
| 出版日期 | 2012 |
| 会议日期 | 2012 |
| 会议地点 | China |
| 关键词 | Target Tracking image Sequences algorithm Design And Analysis |
| 页码 | 331–336 |
| 英文摘要 | Motion blur is very common in the low quality of image sequences and videos captured by low speed of cameras. Object tracking without accounting for the motion blur would easily fail in these kinds of videos. We propose a new data-driven tracker in the particle filter framework to address this problem without deblurring the image sequences. The motion blur is detected by exploring the property of the blurred input image through Fourier analysis. The appearance model is integrated with a set of motion blur kernels which could reflect different blur effects in real scenes. The motion model is improved to be more robust to sudden motion of the target object. To evaluate the proposed algorithm, several challenging videos with significant motion blur are used in the experiments. The experimental results demonstrate the robustness and accuracy of our algorithm. |
| 会议录 | 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12688]  |
| 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 | Jianwei Ding,Kaiqi Huang,Tieniu Tan. Tracking blurred object with data-driven tracker[C]. 见:. China. 2012. |
入库方式： OAI收割
来源：自动化研究所
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