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    李善青, 唐 亮, 刘科研, 王 磊. 一种快速的自适应目标跟踪方法[J]. 计算机研究与发展, 2012, 49(2): 383-391.
    引用本文: 李善青, 唐 亮, 刘科研, 王 磊. 一种快速的自适应目标跟踪方法[J]. 计算机研究与发展, 2012, 49(2): 383-391.
    Li Shanqing, Tang Liang, Liu Keyan, Wang Lei. A Fast and Adaptive Object Tracking Method[J]. Journal of Computer Research and Development, 2012, 49(2): 383-391.
    Citation: Li Shanqing, Tang Liang, Liu Keyan, Wang Lei. A Fast and Adaptive Object Tracking Method[J]. Journal of Computer Research and Development, 2012, 49(2): 383-391.

    一种快速的自适应目标跟踪方法

    A Fast and Adaptive Object Tracking Method

    • 摘要: 由于光照变化、视角差异、相机抖动和部分遮挡等因素的影响,鲁棒的目标跟踪仍然是计算机视觉领域极具挑战性的研究课题.受协同训练和粒子滤波算法的启发,提出一种快速的自适应目标跟踪方法.该方法采用HOG(histogram of oriented gradients)和LBP(local binary pattern)描述目标特征并建立分类器,通过协同训练实现分类器的在线更新,有效解决了误差累积问题.为缩小目标搜索的状态空间,利用ICONDENSATION的运动模型和重要采样提高粒子采样的准确性和效率,并引入校正因子抑制虚假目标的干扰,从而提升了跟踪算法的鲁棒性和分类器更新的准确性.在两组标准测试集和两组自建测试集上的对比实验结果验证了所提出跟踪算法的有效性.与基于全局搜索的跟踪方法相比,该算法在不降低跟踪性能的前提下将处理速度提高25倍以上.

       

      Abstract: Robust object tracking is still a challenging research topic due to dynamical changes of illumination, viewpoints, camera jitter and partial occlusion. To handle such problems, this paper presents an adaptive object tracking method based on co-training and particle filtering algorithms. An online learning scheme with cooperation of two visual classifiers is designed to alleviate the drifting problem. The histogram of oriented gradients (HOG) and local binary pattern (LBP) are used to describe the object appearance. Two SVM classifiers are constructed separately based on the above two visual features, and online updated by the co-training framework. This updating strategy considers the same classification problem from two complementary viewpoints which successfully overcome the error accumulation problem. In order to reduce the searching state space, we introduce a dynamical model and importance sampling from the ICONDENSATION framework to improve the precision and efficiency of the sampling procedure. A correction term is introduced to decrease the weights of false positive samples, and hereby improve the performance of object tracking and classifier updating. Experimental results on four datasets verify the effectiveness and robustness of the proposed adaptive tracking method. Without decreasing the tracking performance, our method also achieves a speedup of 25 times for object state estimation compared with the traditional sliding window algorithm.

       

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