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Tracking blurred object with data-driven tracker
Jianwei Ding; Kaiqi Huang; Tieniu Tan
2012
Conference NameIEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
Source Publication2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
Pages331–336
Conference Date2012
Conference PlaceChina
AbstractMotion 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.
KeywordTarget Tracking   image Sequences   algorithm Design And Analysis
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12688
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Jianwei Ding,Kaiqi Huang,Tieniu Tan. Tracking blurred object with data-driven tracker[C],2012:331–336.
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