一种自适应模板更新的判别式KCF跟踪方法

为了解决单目标跟踪算法中存在的目标旋转、遮挡和快速运动等挑战，提出了一种基于自适应更新策略的判别式核相关滤波器（kernelized correlation filter，KCF）目标跟踪新框架。构建了外观判别式模型，实现跟踪质量有效性的评估。构造了新的自适应模板更新策略，能够有效区分目标跟踪异常时当前目标是否发生了旋转。提出了一种结合目标检测的跟踪新构架，能够进一步有效判别快速运动和遮挡状态。同时，针对上述3种挑战，分别采用模板更新、目标运动位移最小化以及目标检测算法实现目标跟踪框的恢复，保证了跟踪的有效性和长期性。实验分别采用2种传统手动特征HOG和CN（color names）验证提出的框架鲁棒性，结果证明了提出的目标跟踪新方法在速度和精度方面的优越性能。

Adaptive template update of discriminant KCF for visual tracking

To solve the challenges of in-plane/out-of-plane rotation (IPR/OPR), fast motion (FM), and occlusion (OCC), a new robust visual tracking framework of discriminant kernelized correlation filter (KCF) based on adaptive template update strategy is presented in this paper. Specifically, the proposed discriminant models were first used to determine the tracking validity and then a new adaptive template update strategy was introduced to effectively distinguish whether or not the object has rotated when the object tracking was abnormal. Furthermore, a new visual tracking framework combining object test is presented, which could further effectively distinguish FM and OCC. Meanwhile, to overcome the above-mentioned challenges, three measures were taken to recover the object tracking frame:template updating, object movement displacement minimization, and use of an object detection algorithm ensuring validity and long-term visual tracking. We implemented two versions of the proposed tracker with representations from two conventional hand-actuated features, histogram of oriented gradient (HOG), and color names (CN) to validate the strong compatibility of the algorithm. Experimental results demonstrated the state-of-the-art performance in tracking accuracy and speed for processing the cases of IPR/OPR, FM, and OCC.