结合BRISK与区域预估的改进长时跟踪算法

Improved Long Time Tracking Algorithm by Combining BRISK and Region Estimation

鉴于传统的跟踪学习检测(TLD)算法存在稳健性差、跟踪成功率低以及运算效率低等问题,提出一种结合二进制稳健不变可扩展关键点(BRISK)特征点与区域预估的TLD跟踪算法。在跟踪器中引入BRISK特征点,将其与传统的用于跟踪的普通像素点相结合,共同用于目标跟踪,由于BRISK特征点提取较快,从而使得跟踪器部分的总体运算时间降低;在检测器部分采用了卡尔曼滤波器与马尔可夫模型方向预测器相结合的方式,该方式使得最终送入到检测器的子图像块数量大幅缩减,且对相似目标的辨别能力增强,进而提升了检测器的速度和精度。实验结果表明,相比于传统TLD算法,所提TLD算法的跟踪精度提高约64.4%,运行速度提升约39.6%,并具有更好的稳健性。

In view of the fact that the traditional tracking learning detection （TLD） algorithm has poor robustness, low tracking success rate and low computing efficiency, a TLD tracking algorithm combining binary robust invariant scalable keypoints （BRISK） feature points and region prediction is proposed. In the tracker, the BRISK feature point is combined with the conventional pixel points used for tracking, and they are used for target tracking together. Due to the fast extraction of BRISK feature points, the total computing time of the tracker is reduced. In the detector part, the combination of Kalman filter and Markov model direction predictor greatly reduces the number of sub-image blocks sent to the detector, and enhances the identification ahitity for similar targets, thereby improving the speed and accuracy of the detector. The experimental results show that, compared with the traditional TLD algorithm, the tracking accuracy of the proposed TLD algorithm is improved by about 64.4%, and the running speed is increased by about 39.6%, and its robustness is better.