Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

中文翻译：

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基于互补特征的多尺度抗遮挡相关滤波器目标跟踪方法

针对跟踪过程中复杂因素容易引起跟踪漂移的问题，提出一种在核相关滤波器框架下的改进目标跟踪方法。为了获得对对象外观变化不敏感的判别信息，它结合了降维的方向梯度直方图特征和Lab颜色特征，可用于稳健地利用互补特征。基于多分辨率金字塔理论的思想，构建物体的多尺度模型，根据置信图不同大小的响应峰，找到目标跟踪的最优尺度。对于模型存在不适当更新时很容易出现跟踪失败的情况，它根据最佳对象状态对应的响应峰的遮挡率是否小于设定的阈值来检测遮挡。同时，卡尔曼滤波器用于记录被遮挡对象的运动特征信息，预测被遮挡对象的状态，可以实现对被遮挡影响对象的鲁棒跟踪。实验结果表明，该方法在具有挑战性的环境下处理各种内部和外部干扰方面的有效性。可以实现对受遮挡影响的对象的鲁棒跟踪。实验结果表明，该方法在具有挑战性的环境下处理各种内部和外部干扰方面的有效性。可以实现对受遮挡影响的对象的鲁棒跟踪。实验结果表明，该方法在具有挑战性的环境下处理各种内部和外部干扰方面的有效性。