Tracking Blurred Object with Data-Driven Tracker

Jianwei Ding, Kaiqi Huang, T. Tan
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引用次数: 4

Abstract

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.
用数据驱动跟踪器跟踪模糊对象
运动模糊在低质量的图像序列和低速相机拍摄的视频中非常常见。在这类视频中,不考虑运动模糊的对象跟踪很容易失败。我们在粒子滤波框架中提出了一种新的数据驱动跟踪器,在不去模糊图像序列的情况下解决了这个问题。通过傅里叶分析探索模糊输入图像的特性来检测运动模糊。外观模型集成了一组运动模糊核,可以反映真实场景中不同的模糊效果。对运动模型进行了改进,使其对目标物体的突然运动具有更强的鲁棒性。为了评估所提出的算法,在实验中使用了几个具有明显运动模糊的挑战性视频。实验结果证明了该算法的鲁棒性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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