Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.

中文翻译：

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基于自适应重叠相关滤波器的机器人实时认知成像视觉跟踪方法

计算机视觉是认知计算领域中非常重要的研究方向。机器人在计算机视觉系统中遇到各种目标跟踪问题。稳健的比例估计是跟踪算法中的重要问题。大多数可用的方法都难以解决复杂视频中比例的合理变化。在本文中，我们提出了一种基于鲁棒尺度估计的视觉跟踪方法，该方法使用基于基于时间的尺度空间滤波器和自适应互相关滤波器的判别相关滤波器。跟踪器使用单独的基本过滤器进行样品迁移和规模估算。此外，可以将内置的比例估计方法引入其他跟踪算法中。我们在UAV123数据集上验证了提出的方法。传统的相关滤波器跟踪方法的比较实验结果表明，该方法在控制计算复杂度的同时，提高了成功率和跟踪精度。通过曲线下面积测得的成功率为0.638，而在20％的位置误差精度下，成功率为0.649。