基于位置一步预测的核相关目标跟踪算法

Kernel Correlation Target Tracking Algorithm Based on One-step Position Prediction

针对视频机动目标跟踪过程中前后两视频帧间目标移动距离过大导致目标丢失问题,提出一种基于位置一步预测的核相关目标跟踪算法.首先,提取当前帧目标区域灰度以及颜色特征,并分别对灰度特征和颜色特征进行梯度及颜色直方图运算,得到FHOG特征向量和颜色直方图;然后根据颜色直方图引入粒子滤波对下一帧图像中目标位置进行一步预测;进而,以预测到的目标位置为中心确定搜索区域;最终,在搜索区域基于FHOG特征结合核相关滤波修正当前目标位置的估计值.在此基础上,针对机动目标移动过程中产生的形变以及模糊问题,结合零截距与平均峰值相关能量,实现对形变和模糊问题的处理.实验结果表明,提出的新方法可以有效地提高在UADETRAC数据集的识别率,相比标准核相关滤波算法在成功率与准确率上分别提高11.96%与9.6%.

To solve the problem of target loss caused by the large moving distance between two video frames during maneuvering target tracking in a video, this study proposes a kernel correlation target tracking algorithm based on onestep position prediction. Firstly, the gray scale and color feature of the target region in the current frame are extracted, and the gradient and color histogram operations of the gray scale feature and color feature are carried out respectively to obtain the FHOG feature vector and color histogram. Then, according to the color histogram, particle filter is introduced to predict the target position in the next frame. Further, the search area is determined with the predicted target position at the core. Finally, the estimated value of the current target position is corrected depending on the FHOG feature and the kernel correlation filtering in the search area. On this basis, the deformation and ambiguity generated during the movement of the maneuvering target are dealt with by the combination of the zero intercept and the average peak-tocorrelation energy. Experimental results show that the proposed method can effectively improve the recognition rate in the UA-DETRAC dataset, and the success rate and accuracy of the proposed method are 11.96% and 9.6% higher than those of the standard kernel correlation filter algorithm.