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Robust Object Tracking Based on Simplified Codebook Masked Camshift Algorithm

机译：基于简化码本掩盖Camshift算法的鲁棒目标跟踪

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摘要

Moving targets detection and tracking is an important and basic issue in the field of intelligent video surveillance. The classical Codebook algorithm is simplified in this paper by introducing the average intensity into the Codebook model instead of the original minimal and maximal intensities. And a hierarchical matching method between the current pixel and codeword is also proposed according to the average intensity in the high and low intensity areas, respectively. Based on the simplified Codebook algorithm, this paper then proposes a robust object tracking algorithm called Simplified Codebook Masked Camshift algorithm (SCMC algorithm), which combines the simplified Codebook algorithm and Camshift algorithm together. It is designed to overcome the sensitiveness of traditional Camshift algorithm to background color interference. It uses simplified Codebook to detect moving objects, whose result is employed to mask color probability distribution image, based on which we then use Camshift to predict the centroid and size of these objects. Experiment results show that the proposed simplified Codebook algorithm simultaneously improves the detection accuracy and computational efficiency. And they also show that the SCMC algorithm can significantly reduce the possibility of false convergence and result in a higher correct tracking rate, as compared with the traditional Camshift algorithm.
机译：运动目标的检测和跟踪是智能视频监控领域的重要而基本的问题。通过将平均强度而不是原始的最小和最大强度引入到Codebook模型中，简化了经典Codebook算法。并根据高，低强度区域的平均强度，提出了当前像素与码字之间的分层匹配方法。在简化码本算法的基础上，提出了一种鲁棒的目标跟踪算法，称为简化码本掩盖的Camshift算法（SCMC算法），将简化码本算法和Camshift算法结合在一起。它旨在克服传统的Camshift算法对背景颜色干扰的敏感性。它使用简化的Codebook来检测运动物体，然后将其结果用于掩盖颜色概率分布图像，然后基于Camshift预测这些物体的质心和尺寸。实验结果表明，所提出的简化码本算法同时提高了检测精度和计算效率。并且他们还表明，与传统的Camshift算法相比，SCMC算法可以大大减少错误收敛的可能性，并获得更高的正确跟踪率。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第13期|376494.1-376494.12|共12页
  • 作者

    Zhang Yuanyuan; Zhao Xiaomei; Li Fengjiao; Sun Jiande; Jiang Shuming; Chen Changying;

  • 作者单位

    Shandong Acad Sci, Informat Res Inst, Jinan 250014, Peoples R China.;

    Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China.;

    Shandong Acad Sci, Informat Res Inst, Jinan 250014, Peoples R China.;

    Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.;

    Shandong Acad Sci, Informat Res Inst, Jinan 250014, Peoples R China.;

    Shandong Acad Sci, Informat Res Inst, Jinan 250014, Peoples R China.;

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