| 李建良,张婷婷,陶知非,李淑清,郭秋蕊.基于改进 Camshift 与 Kalman 滤波
融合的领航车辆跟踪算法[J].电子测量与仪器学报,2021,35(6):131-139 |
| 基于改进 Camshift 与 Kalman 滤波
融合的领航车辆跟踪算法 |
| Pilot vehicle tracking algorithm based on improvedCamshift and Kalman filter fusion |
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| DOI： |
| 中文关键词: 领航车辆跟踪 Camshift 算法 Bhattachayya 系数 Kalman 滤波 边缘梯度 |
| 英文关键词:pilot vehicle tracking Camshift algorithm Bhattachayya coefficient Kalman filter edge gradient |
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| 摘要点击次数: 314 |
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| 中文摘要: |
| 针对智能车辆在对前方领航车辆进行视觉跟踪时,传统的 Camshift 算法容易受目标突然变速、相似颜色背景或目标干
扰的问题,提出一种基于改进 Camshift 与 Kalman 滤波融合的领航车辆跟踪算法。 该算法通过提取目标模板的色度、饱和度和
Canny 边缘梯度幅值 3 个特征分量,建立三维直方图并对其反向投影进行跟踪,同时采用 Bhattachayya 系数作为目标跟踪准确
性的判别依据。 若系数大于设定阈值则判定目标跟踪不准确,此时用局部二值模式(LBP)级联分类器对领航车辆进行检测识
别,最后引入 Kalman 滤波器来预测下一帧领航车辆的位置。 实验结果表明,该算法能够在复杂背景下对领航车辆进行实时并
有效的跟踪。 |
| 英文摘要: |
| Aiming at the problem that the traditional Camshift algorithm is susceptible to the sudden acceleration and deceleration of the
target and the background or the target interference of the similar colors when the intelligent vehicle is visually tracking the pilot vehicle
in front, a pilot vehicle tracking algorithm that combines the improved Camshift and Kalman filter was proposed. The algorithm tracked
the back projection of the three-dimensional histogram established by the target template hue, saturation, and edge gradient amplitude
feature components. The Bhattachayya coefficient was used as the basis for determining the accuracy of target tracking. If the coefficient
was greater than the set threshold, the target tracking would be judged to be inaccurate. At this time, the LBP cascade classifier was
used to detect and recognize the pilot vehicle, and finally the Kalman filter was introduced to predict the position of the pilot vehicle in
the next frame. The experimental results demonstrate that the proposed algorithm can accurately track the pilot vehicle in real time in a
complex background. |
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