基于正则化卷积神经网络的目标跟踪算法

Target tracking algorithm based on regularized convolution neural network

为了提高目标物体的跟踪鲁棒性和稳定性,文中将L2正则化最小二乘法和卷积神经网络(CNN)相互结合,提出了一种基于正则化卷积神经网络的目标跟踪算法。通过L2跟踪器来评估目标无题被遮挡的程度,利用两层CNN对目标进行目标表示,去除了大部分无关样本,降低了算法的复杂度。实验结果表明,当目标物体发生姿态变化或旋转等剧烈的外观变化时,所提算法具有较强的鲁棒性和稳定性,并且比其他经典的跟踪算法具有更高的精度。

In order to improve the tracking robustness and stability of the target object,a target tracking algorithm based on regularized convolution neural network( CNN) was proposed in this paper,which combines L2 regularized least squares method with convolution neural network( CNN). L2 tracker was used to evaluate the degree of occlusion of the target. Two-layer CNN was used to represent the target robustly. Most of the irrelevant samples were removed and the complexity of the algorithm was reduced.The experimental results show that the proposed algorithm has strong robustness and stability,and has higher accuracy than other classical tracking algorithms when the object changes its formation dramatically.