激光与光电子学进展, 2019, 56 (16): 161505, 网络出版: 2019-08-05
基于概率模型的自适应融合互补学习跟踪算法 下载: 878次
Adaptive Merging Complementary Learners for Visual Tracking Based on Probabilistic Model
机器视觉 视觉跟踪 概率模型 融合系数 分段函数 machine vision visual tracking probabilistic model merging coefficient piecewise function
摘要
在互补学习实时跟踪算法(Staple)中,方向梯度直方图(HOG)特征与颜色直方图采用的融合系数均为固定值0.3,在不同特征下相融时易造成目标丢失的问题。基于此,提出一种基于目标概率模型的自适应融合互补学习实时跟踪算法(amStaple),该算法使用分段函数得出自适应融合系数。分别在OTB-2013与OTB-100基准视频集上对所提算法进行实验测试,最终的实验结果显示,本文算法极大地提升了跟踪器性能,与Staple相比,在两个基准数据集上其精度分别高出6.52%与3.32%,成功率分别高出4.89%与3.11%。本文算法较为简单,且在与同时期优秀算法的定性与定量比较中表现较优。为解决本文提出的算法在基准视频部分属性上表现欠佳的问题,在本文算法的基础上增加判定条件,提出了amStaple1算法。
Abstract
In complementary learners for real-time tracking known as Staple, the merging coefficients of histogram of oriented gradient feature and color histogram have both a fixed value of 0.3, which can easily cause the problem of losing target when they are merged under different features. To solve this problem, this study proposes an adaptive merging algorithm of complementary learners for real-time visual tracking based on an object probabilistic model known as amStaple, which uses a piecewise function to obtain the adaptive merging coefficient. Experiments on popular object tracker benchmarks including OTB-2013 and OTB-100 verify the effectiveness of the proposed algorithm. Results show that amStaple has better performance than Staple. Compared with Staple in terms of OTB-2013 and OTB-100, amStaple has 6.52% and 3.32% higher precision and 4.89% and 3.11% higher success rates, respectively. Although the proposed algorithm is relatively less innovative, its performance has been obviously improved in various aspects compared with that of a state-of-the-art algorithm from the same period. However, amStaple performs poorly on partial sequence attributes of object tracker benchmarks. To solve this problem, a decision condition is added based on amStaple, which is called amStaple1.
董秋杰, 何雪东, 葛海燕, 周盛宗. 基于概率模型的自适应融合互补学习跟踪算法[J]. 激光与光电子学进展, 2019, 56(16): 161505. Qiujie Dong, Xuedong He, Haiyan Ge, Shengzong Zhou. Adaptive Merging Complementary Learners for Visual Tracking Based on Probabilistic Model[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161505.