通道和异常适应性的目标跟踪算法

Object Tracking Algorithm with Channel and Anomaly Adaptation

针对现有空间正则化跟踪算法未考虑通道和异常适应性,在光照变化、遮挡和运动模糊等复杂跟踪场景下易产生跟踪失败的问题,提出通道和异常适应性的目标跟踪算法。首先,提取目标所在区域的方向梯度直方图和颜色特征,建立目标的外观模型;其次,提出通道加权策略并构造通道适应性正则项,同时在模型训练阶段优化通道权重,降低多通道特征中冗余信息和通道可靠性变化对跟踪性能的影响;然后,构造异常适应性正则项,通过约束跟踪响应图异常变化,提升画面快速变化时跟踪器的鲁棒性;最后,在检测阶段将滤波器与当前帧的样本相关运算得到目标尺度和位置信息,通过分析响应图的峰值与噪声平滑度来判断跟踪的遮挡情况以过滤低质量样本,增强目标被遮挡时跟踪器的异常适应性。在OTB50、OTB100和TC-128公共数据集上与多种先进算法进行对比实验,实验结果表明,所提算法在光照变化、遮挡、运动模糊等复杂场景下鲁棒性表现更好,跟踪成功率高于同类算法,并且精度更优。

The spatial regularization tracking algorithm ignores channel and anomalies adaptation,which can easily lead to tracking failure in complex tracking scenarios such as illumination changes,occlusions,and motion blur.To address these problems,this paper proposes a tracking algorithm with adaptive channel and anomaly adaptation.Firstly,the appearance model of the target is built by the gradient and color features.Secondly,a channel weighting strategy is proposed.An adaptive channel regularizer is constructed to optimize the channel weights simultaneously in the training phase to reduce the impact of redundant information in multi-channel features and channel reliability changes on the tracking performance.Then,the adaptive anomaly regularizer is constructed to constrain the response map’s abnormal changes and improve the tracker’s robustness when the region changes rapidly.Finally,the filter is correlated with the current sample to obtain the target scale and position in the detection stage.The peak-versusnoise smoothness index of the response map is calculated to judge the occlusion and exclude low-quality samples to enhance the abnormal adaptation when occlusion occurs.Comparative experiments with several mainstream methods are performed on the OTB50,OTB100 and TC-128 benchmarks.Experimental results show that the proposed algorithm has better robustness in complex scenarios such as illumination variation,occlusion,motion blur,etc.The tracking success rate is higher than similar algorithms,and the accuracy is higher.