多目标跟踪是视频监控等领域的一项关键技术，该文提出一种基于主颜色的多目标跟踪算法，在算法中使用主颜色描述感兴趣目标，在卡尔曼滤波器预测的基础上利用基于主颜色的mean shift算法对各目标进行跟踪，接着利用目标跟踪位置与前景blob之间的关联矩阵来推理多目标跟踪问题中的各种情况，根据不同的情况对目标的位置、大小以及颜色信息做相应的更新。对大量图像序列的测试结果表明，该算法能够较好地处理遮挡，具有稳健的跟踪效果。

Multi-object tracking is one of the key technologies in video surveillance. In this paper, a new PCR-based multi-object tracking algorithm is proposed and principal colors are used to characterize the appearance of each object. Combined with the kalman filter prediction a PCR-based mean shift procedure is applied to estimate the current location of each object. Then an association matrix between the estimate locations and foreground blobs is built to distinguish many different situations that possibly occur in complex real world applications. Lastly the state of object including location, size and color model is updated according to different situations. Experimental results on many image sequences have shown that the proposed algorithm can efficiently deal with complex occlusions. The performance of this new tracking algorithm is robust.