改进STC和SURF特征联合优化的目标跟踪算法

针对传统时空上下文目标跟踪(STC)算法中目标窗口不能适应目标尺度变化,导致对目标针对性不强等问题,提出改进STC和SURF特征联合优化的目标跟踪算法(STC-SURF)。首先利用加速稳健(SURF)特征算法对相邻的2帧图像提取特征点并进行匹配,再通过随机抽样一致(RANSAC)算法消除误匹配,提高匹配精度。进而根据2帧图像中匹配特征点的变化对目标窗口进行调整。最终对STC算法中模型的更新方式进行优化以提高跟踪结果的准确性。实验结果表明,STC-SURF算法能够适应目标尺度变化,并且其目标跟踪成功率优于TLD算法和传统STC算法的。

A target tracking algorithm based on jointoptimization of improved STC and SURF features

Aiming at the problem that the target window cannot adapt to target scale change in the traditional spatio-temporal context tracking (STC) algorithm, which leads to inaccurate targeting, we propose a target tracking algorithm based on joint optimization of improved STC and SURF features (STC-SURF). Firstly, the feature points of two adjacent frames are extracted and matched by the speeded up robust feature (SURF) algorithm, and the random sample consensus (RANSAC) matching algorithm is used to eliminate the mismatch and increase the matching precision. Furthermore, the target window is adjusted according to the change of the matching feature points in the two frames of the image, and then outputted. Finally, the update method of the model of the STC algorithm is optimized to increase the accuracy of the tracking result. Experimental results show that the STC-SURF algorithm can adapt to the target scale change, and the target tracking success rate is better than the target-learning detection (TLD) algorithm and the traditional STC algorithm.