Continuously Adaptive Mean-shift Tracking Algorithm with Suppressed Background Histogram Model
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摘要: 针对传统连续自适应均值漂移(CAMshift)跟踪算法在建立目标颜色模型阶段容易包含大量背景颜色信息从而使跟踪效果变差的问题，该文提出一种改进算法。利用混合高斯模型背景法(GMM)将原始图像分割成前景和背景的叠加，在原始图像和背景图像上运动物体所在区域分别建立色调分量直方图，利用背景图像的色调分量直方图计算原始图像中对应色调分量的权值，抑制原始图像中与背景颜色相同的色调，扩大前景与背景颜色的差异性。该方法通过对原始颜色模型中属于背景的色调进行抑制，扩大了目标颜色模型的显著性，提高了跟踪的准确性和稳定性，目标定位的最大中心误差小于20%，能够准确跟踪不发生丢失。
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关键词:
- 目标跟踪 /
- 背景抑制 /
- 直方图 /
- 色调 /
- 连续自适应均值漂移跟踪算法
Abstract: For the deficiency of traditional Continuously Adaptive Mean-shift (CAMshift) tracking algorithm can easily contain a large number of color information which belongs to the background in the process of establishing the target color model, an improved algorithm is proposed. The original image is divided into foreground and background based on the Gaussian Mixture Model(GMM). In the original image and the background image, the histogram of the hue component is established. Hue histograms of the background image are used to calculate the weight of the hue component in the original image. The hues belonging to the background are suppressed and the color differences between foreground and background are expanded. Experiment shows that by suppressing the hue components belonging to the background, the saliency of the target color model is expanded. The accuracy and stability of the target recognition are improved. The ratio of the max deviation to the target is less than 20%, which ensures the target not to be lost. -
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