He Haiyu, Chen Zhen, Liu Haikuo, Liu Xiangdong, Guo Youguang, Li Jian
School of Automation, Beijing Institute of Technology, Beijing, China.
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Cyborg Bionic Syst. 2023 Aug 29;4:0050. doi: 10.34133/cbsystems.0050. eCollection 2023.
Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.
视觉跟踪是仿生机器人感知环境和控制其运动的一项关键技能。然而，当目标由于安装在机器人上的摄像头视角变化而发生非刚性变形时，视觉跟踪具有挑战性。本文提出了一种基于最佳伙伴相似度（BBS）的实时且尺度自适应视觉跟踪方法，BBS是一种能够处理非刚性变形的先进模板匹配方法。所提出的方法在四个方面改进了原始的BBS：（a）优化缓存方案以减少计算开销，（b）从理论上分析了杂乱背景对BBS的影响，并引入基于块的纹理以提高鲁棒性和准确性，（c）使用批量梯度下降算法进一步加速该方法，以及（d）应用重采样策略以使BBS能够在尺度空间中跟踪目标。对所提出的方法在具有挑战性的真实世界数据集上进行了评估，并展示了其良好的性能。
