为了解决传统Mean Shift跟踪方法中目标模板只能从单一图像建立,且很难更新问题,提出了一种新的MeanShift彩色图像跟踪方法.将RGB颜色空间投影到HSV颜色空间,建立了基于HSV颜色空间的统一直方图核函数模型.为了实现模板在线更新,引入在线支持向量机,推理了基于HSV空间的在线支持向量机的Mean Shift跟踪算法,从而适应目标因尺寸、姿态及光照造成的模型变化.为了验证算法的有效性,对两组国际通用的CAVIAR彩色图像序列进行了跟踪测试.实验结果表明,提出的改进算法在目标姿态、光照或背景发生较大变化时,能有效跟踪目标.当图像分辨率为384×288(目标尺寸约为20×80)时,最快处理速度达40 f/s,且跟踪精度比传统Mean Shift提高32.1％.
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