针对模型匹配跟踪算法易受遮挡、复杂背景等因素影响的问题,提出判别外观模型下的寻优匹配跟踪算法.首先,提取前5帧图像的局部特征块,建立由特征块组成的训练样本集,并利用颜色、纹理特征进行聚类组建判别外观模型.然后,利用双向最优相似匹配方法进行目标检测.为了解决复杂背景干扰,提出前景划分方法约束匹配过程,得到更准确的匹配结果.最后,定期将跟踪结果加入聚类集合以更新外观模型.实验表明,由于利用多帧训练的判别外观模型及双向最优相似匹配方法,算法在局部遮挡、复杂背景等条件下的跟踪准确率较高.%Traditional model matching and tracking algorithms are easily influenced by the occlusion of other targets and the complex background. To solve these problems, an optimal matching tracking algorithm based on discriminant appearance model is proposed. Firstly, the local feature blocks of the previous 5 frames of the image sequences are extracted by sampling, and the training sample set consisting of a number of feature blocks is established. Then, the feature blocks with the same color and texture features are clustered to build a discriminant appearance model. Secondly, the bi-directional optimal similarity matching method is adopted for target detection. To avoid complex background interference, a method of foreground partition is proposed to acquire more accurate matching results. Finally, the tracking results are periodically added to the clustering collection to update the appearance model. The experimental results indicate that the proposed approach provides higher tracking accuracy under the conditions of partial occlusion and complex background by using the discriminant appearance model of multi-frame training and the bi-directional optimal similarity matching method.
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