基于特征融合的改进LCT跟踪算法

Improved LCT Tracking Algorithm Based on Feature Fusion

为解决LCT算法在目标形变与快速移动情况下跟踪效果差的问题,提出一种基于特征融合的跟踪算法。在梯度方向直方图特征相关滤波的基础上,提取目标与背景颜色直方图特征,得到颜色特征的目标预测位置。在此基础上,根据跟踪置信度确定特征融合权重,综合考虑梯度特征与颜色特征得到跟踪结果。实验结果证明,与LCT算法相比,该算法的距离精度和重叠精度分别提高了11.5 %和21.2 %,平均中心位置误差减少了15.3像素。

To address the poor tracking performance of the LCT algorithm when the target is in deformation or fast moving,a tracking algorithm based on feature fusion is proposed.Based on the Histogram of Oriented Gradient(HOG) feature correlation filtering,the target and background color histogram features are extracted to obtain the target prediction position of the color feature.On the basis,the feature fusion weight is determined according to the tracking confidence.Tracking results are obtained by considering gradient features and color features.Experimental results show that compared with the LCT algorithm,the distance accuracy and overlap accuracy of the algorithm are increased by 11.5 % and 21.2 %,respectively,and the average center position error is reduced by 15.3 pixels.