结合孪生网络重检的长期目标跟踪算法

针对传统长期相关滤波器使用特征单一、跟踪失败后无法再次捕捉到目标的缺点, 提出一种结合深度学习的多特征融合长期目标跟踪算法. 本算法在长期相关跟踪算法(long-term correlation tracking, LCT))的基础上, 采用多特征融合的方式, 将局部二值模式特征、改进的方向梯度直方图特征以及颜色特征相融合, 来提高跟踪算法的鲁棒性. 由于LCT算法选择随机蕨分类器进行目标重检, 对检测范围有局限性且重检精度较低, 故采用基于深度学习的孪生网络实例搜索(SINT)方法对全局图像进行目标重检. 本文的实验在OTB100数据集上进行, 结果表明: 本文算法与LCT算法相比, 距离精度和成功率分别提升了13%和10.3%.

Long-term Target Tracking Algorithm Based on Twin Network Rechecking

The traditional long-term correlation filter uses a single feature and cannot capture the target again after tracking failure. Considering this, the paper proposes a multi-feature fusion long-term target tracking algorithm combined with deep learning. On the basis of the long-term correlation tracking (LTC) algorithm, the proposed algorithm uses multi-feature fusion to join together the local binary pattern feature, the improved directional gradient histogram feature, and the color feature to promote the robustness of the tracking algorithm. Since the LCT algorithm adopts a random fern classifier to recheck the target, which has a limited detection range and low rechecking accuracy, the deep learning-based twin network instance search (SINT) method is employed to recheck the global image. The experiment in this paper is carried out on the OTC100 dataset, and the results show that compared with the LCT algorithm, the proposed algorithm has improved the range accuracy and the success rate by 13% and 10.3% respectively.