一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法

Background-aware Correlation Filter Tracking Algorithm with Adaptive Scaling and Learning Rate Adjustment

针对跟踪过程中遮挡因素以及目标尺度变化因素导致的目标跟踪漂移问题,文中提出了一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法。该算法首先通过背景感知相关滤波器获得目标的初步位置信息;其次在背景感知相关滤波器的基础框架下训练尺度相关滤波器,以有效估计目标尺度变化,从而准确调整搜索区域的大小;然后根据响应图波动情况进行遮挡判定,利用平均峰值能量指标与最大响应值判定目标遮挡情况,自适应调整模型学习速率大小;最后,设计相应的模型更新策略,来提高模型性能。在OTB100 Benchmark数据集上进行测试,实验结果表明,该算法与背景感知相关滤波器相比,其成功率提高了6.2%,精度提高了10.1%,因此该算法能有效地处理遮挡、尺度变化等问题,提高了跟踪模型的成功率与准确率,同时具有实时的跟踪速度。

Aiming at the problem of object tracking drift caused by occlusion factors and target scale changes during the tracking process,this paper proposes an adaptive scale and learning rate-adjusted background-aware correlation filter tracking algorithm.First,this algorithm obtains the target’s initial position information through the background-aware correlation filter;then,it trains the scale correlation filter under the basic framework of the background-aware correlation filter and estimates the target scale change effectively,thus accurately adjusting the search area size;next,the occlusion determination is performed according to the fluctuation of the response map,and the average peak energy index and the maximum response value are used to estimate target occlusion,thus enabling the model to adaptively adjust learning rate;finally,this algorithm designs the corresponding model update strategy to improve the model performance.This algorithm is tested on the OTB100 Benchmark dataset,and test result show that this algorithm improves the success rate by 6.2%and the accuracy by 10.1%compared with the background-aware correlation filter.Therefore,the proposed algorithm can effectively deal with occlusion and scale changes,improve the success rate and accuracy of the tracking model,and have a real-time tracking speed.