融合KCF和HOG的改进TLD目标跟踪算法

Fusion of KCF and HOG Based Improvement TLD Target Tracking Algorithm

针对跟踪学习检测(TLD)算法跟踪速度慢,对光照变化鲁棒性差的问题,提出了一种融合核相关滤波(KCF)和方向梯度直方图(HOG)的改进TLD目标跟踪算法。该算法将TLD跟踪模块的中值流跟踪替换为KCF目标跟踪,通过循环矩阵将计算从时域转换到频域,大大降低了计算量,提高了算法的跟踪速度;再通过提取目标的HOG特征,代替TLD检测模块中随机森林检测器的灰度特征,通过对图像的归一化,降低光照强度对检测的影响,增加了检测器检测成功率,提高了算法的鲁棒性。实验表明,改进TLD算法的一次性通过评估(OPE)精确度达78.7%,成功率达74%;在光照变化下的OPE精确度和成功率也高出TLD算法15%以上;测试视频的跟踪速度达TLD算法的2倍,具有较好的实时目标跟踪能力。

Aiming at the problem that the tracking-learning-detection(TLD)algorithm has a slow tracking speed and poor robustness to illumination changes,an improved TLD target tracking algorithm combining kernel correlation filtering(KCF)and directional gradient histogram(HOG)is proposed.This algorithm replaces the median flow tracking of the TLD tracking module with KCF target tracking,and converts the calculation from the time domain to the frequency domain through the cyclic matrix,which greatly reduces the amount of calculation and improves the tracking speed of the algorithm;and then extracts the HOG f eatures of the target instead of the gray feature of the random forest detector in the TLD detection module,the normalization of the image reduces the impact of light intensity on the detection,increases the detection success rate of the detector,and improves the robustness of the algorithm.Experiments show that the one-time pass evaluation(OPE)accuracy of the improved TLD algorithm reaches 78.7%,and the success rate reaches 74%;the accuracy and success rate of OPE under light changes are also 15%higher than that of the TLD algorithm;test video tracking has twice the speed of the TLD algorithm and has good real-time target tracking capabilities.