论文标题
转换跟踪模型预测
Transforming Model Prediction for Tracking
论文作者
论文摘要
基于优化的跟踪方法通过整合目标模型预测模块，通过最小化目标函数来提供有效的全局推理，从而获得了广泛的成功。尽管这种归纳偏见整合了有价值的领域知识，但它限制了跟踪网络的表现力。因此，在这项工作中，我们建议采用基于变压器的模型预测模块的跟踪器体系结构。变形金刚几乎没有归纳偏见捕获全球关系，从而可以学习更强大的目标模型的预测。我们进一步扩展了模型预测变量，以估算第二组重量的重量框回归。最终的跟踪器依赖于训练和测试框架信息，以预测所有权重。我们通过在多个跟踪数据集上进行全面的实验来训练拟议的跟踪器端到端，并验证其性能。我们的跟踪器在三个基准测试基准上设定了新的最新技术，在具有挑战性的Lasot数据集上获得了68.5％的AUC。
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function. While this inductive bias integrates valuable domain knowledge, it limits the expressivity of the tracking network. In this work, we therefore propose a tracker architecture employing a Transformer-based model prediction module. Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We further extend the model predictor to estimate a second set of weights that are applied for accurate bounding box regression. The resulting tracker relies on training and on test frame information in order to predict all weights transductively. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.
