Sun Wei
School of Aerospace Science and Technology, Xidian University, No. 2 Tabai Rd., Xi'an 710071, China.
Optik (Stuttg). 2015 Oct;126(19):1830-1837. doi: 10.1016/j.ijleo.2015.05.018. Epub 2015 May 15.
We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.
我们提出了一种基于在线学习方案的新型目标跟踪框架，该框架能够在具有挑战性的场景中稳健运行。首先，提出了一种基于颜色和边缘特征的基于学习的粒子滤波器。我们使用目标和背景信息训练一个支持向量机（SVM）分类器，并将输出映射为概率，然后通过概率输出计算粒子滤波器中粒子的权重，以估计目标的状态。其次，跟踪循环从Lucas-Kanade（LK）仿射模板匹配开始，随后是基于学习的粒子滤波器跟踪。Lucas-Kanade方法估计误差并在正样本数据集中更新目标模板，如果LK跟踪器丢失目标，则基于学习的粒子滤波器跟踪器将启动。最后，SVM分类器评估每个跟踪到的外观，以更新训练集或在必要时重新启动跟踪循环。实验结果表明，我们的方法对具有挑战性的光照、尺度和姿态变化具有鲁棒性，并且在eButton图像序列上的测试也取得了令人满意的跟踪性能。
