检测区域动态调整的TLD目标跟踪算法

针对经典跟踪-学习-检测(TLD)目标跟踪算法由于检测区域过大而导致的检测时间过长及对相似目标跟踪处理效果不理想的问题,提出一种检测区域可动态自适应调整的方法——TLD-DO。该方法利用两次Kalman滤波加速度矫正预测的检测区域优化算法DKF,通过缩小TLD检测器检测范围,以达到在跟踪精度略有提升的情况下提高跟踪速度的目的;同时此方法可排除画面内相似目标的干扰,提高在含有相似目标的复杂背景下目标跟踪的准确性。实验结果表明:TLD-DO算法在处理不同视频与跟踪目标时,检测速度有1.31~3.19倍提升;对含有相似目标干扰情况下,跟踪效果明显优于原TLD算法;对目标抖动及失真情况有较高的鲁棒性。

Improved TLD target tracking algorithm based on automatic adjustment of surveyed areas

There is a long time detection problem caused by too large surveyed area in the classical Tracking-Learning-Detection (TLD) target tracking algorithm. Moreover, the TLD algorithm could not do the similar targets processing well. So in this paper, an efficient approach called TLD-DO was proposed for tracking targets in which the surveyed areas could be automatically adjusted according to the target's velocity of movement. In order to accelerate the process speed of TLD algorithm without reducing tracking precision, a novel algorithm named Double Kalman Filter (DKF) with optimal surveyed area which could reduce the detection range of TLD detector was constructed based on twice Kalman filtering operation for acceleration correction. Meanwhile, the improved method could also increase the accuracy of target tracking through eliminating the interference of the similar targets in complex background. The experimental results show that tracking effect of improved method is better than that of the original TLD algorithm under the circumstance of similar target disturbance. Furthermore, the detection speed has been improved 1.31-3.19 times for different videos and scenes. In addition, the improved method is robust to target vibration or distortion.