论文标题
通过改进的判别模型预测，可靠的长期对象跟踪
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
论文作者
论文摘要
我们提出了一种基于预先训练的短期跟踪器的稳健长期跟踪的改进的区分模型预测方法。基线预训练的短期跟踪器是SuperDimp，它将PRDIMP的边界盒回归器与标准DIMP分类器结合在一起。我们的跟踪器RLT-DIMP在以下三个方面改善了SuperDimp：（1）使用随机擦除的不确定性降低：为了使模型稳健，我们在擦除随机的小矩形区域后从多个图像中利用一致性。然后，我们相应地纠正了模型的跟踪状态。 （2）具有时空约束的随机搜索：我们提出了一种可靠的随机搜索方法，并采用得分惩罚来防止距离突然检测的问题。 （3）背景增强，以进行更歧视的特征学习：我们增强了在搜索区域中未包含的各种背景，以在背景混乱中训练更强大的模型。在对fot-LT2020基准数据集的实验中，所提出的方法可实现与最先进的长期跟踪器相当的性能。源代码可在以下网址获得：https：//github.com/bismex/rlt-dimp。
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.
