Particle Filter Algorithm for Object Tracking Based on Color Local Entropy

Particle Filter Algorithm for Object Tracking Based on Color Local Entropy
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基于颜色局部熵的粒子滤波目标跟踪算法

DOI:
10.1155/2013/961019
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发表时间:
2013
影响因子:
2.1
通讯作者:
Dai Yaping
Dai Yaping
中科院分区:
工程技术4区
文献类型:
--
作者:
Wang Huan;Wang Qinglin;Li Yuan;Dai Yaping

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为了实现准确的视觉目标跟踪,克服物体变形、遮挡和光照变化带来的困难,提出了一种基于颜色局部熵(CLE)的粒子滤波目标跟踪算法。首先利用尺度因子对传统的直方图加权函数进行改进。然后,针对颜色特征对光照和环境干扰敏感的缺点,通过将目标从颜色特征空间映射到局部熵空间,构建了颜色局部熵目标观测模型;此外,设计了目标模板的自适应更新策略,并根据跟踪性能动态调整粒子数。实验结果表明,与现有的几种算法相比,该算法对于光照变化、物体遮挡和非线性运动情况下的实时目标跟踪具有更高的鲁棒性和有效性。
To achieve accurate visual object tracking and overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a particle filter for object tracking algorithm based on color local entropy (CLE) is proposed. First we improved the traditional histogram weighted function by using a scale factor. Then, for the shortcoming that the color feature is sensitive to illumination and environmental interference, a color local entropy object observation model is constructed by mapping the object from color feature space to local entropy space. In addition, an adaptive updating strategy of the object template is designed and the number of particle is adjusted dynamically according to the tracking performance. The experimental results show that compared with several existing algorithms, the proposed algorithm is more effective and robust for the real-time object tracking under the situation of illumination variation, object occlusion, and nonlinear motion.
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