We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the following three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple images after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Random search with spatio-temporal constraints: we propose a robust random search method with a score penalty applied to prevent the problem of sudden detection at a distance. (3) Background augmentation for more discriminative feature learning: We augment various backgrounds that are not included in the search area to train a more robust model in the background clutter. In experiments on the VOT-LT2020 benchmark dataset, the proposed method achieves comparable performance to the state-of-the-art long-term trackers. The source code is available at: https://github.com/bismex/RLT-DIMP.

本研究提出了一种改进的判别式模型预测方法，用于长期跟踪，基于预训练的短期跟踪器结合不同的技巧，通过多种手段，包括随机擦除，带空间-时间约束的随机搜索和背景增强等，实现了对跟踪的改进。在VOT-LT2020基准数据集上进行了实验，与最先进的长期跟踪器相比具有可比较的性能。

通过改进辨别模型预测实现鲁棒的长期目标跟踪