Tracking blurred object with data-driven tracker

CASIA OpenIR > 模式识别实验室

	Tracking blurred object with data-driven tracker
	Jianwei Ding; Kaiqi Huang; Tieniu Tan
	2012
会议名称	IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
会议录名称	2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
页码	331–336
会议日期	2012
会议地点	China
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.
关键词	Target Tracking image Sequences algorithm Design And Analysis
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12688
专题	模式识别实验室
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
通讯作者单位	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],2012:331–336.