Video Text Spotting (VTS) is a fundamental visual task that aims to predict the trajectories and content of texts in a video. Previous works usually conduct local associations and apply IoU-based distance and complex post-processing procedures to boost performance, ignoring the abundant temporal information and the morphological characteristics in VTS. In this paper, we propose a novel Global Video Text Spotting Transformer GloTSFormer to model the tracking problem as global associations and utilize the Gaussian Wasserstein distance to guide the morphological correlation between frames. Our main contributions can be summarized as three folds. 1). We propose a Transformer-based global tracking method GloTSFormer for VTS and associate multiple frames simultaneously. 2). We introduce a Wasserstein distance-based method to conduct positional associations between frames. 3). We conduct extensive experiments on public datasets. On the ICDAR2015 video dataset, GloTSFormer achieves 56.0 MOTA with 4.6 absolute improvement compared with the previous SOTA method and outperforms the previous Transformer-based method by a significant 8.3 MOTA.

翻译：视频文本检测跟踪旨在预测视频中文本的运动轨迹和内容，是一项基础性视觉任务。现有方法通常采用局部关联策略，并基于交并比距离和复杂的后处理流程来提升性能，但忽略了视频中丰富的时序信息和文本形态特征。为此，本文提出了一种新颖的全局视频文本检测跟踪Transformer——GloTSFormer，将跟踪问题建模为全局关联，并利用高斯Wasserstein距离引导帧间形态相关性。主要贡献可归纳为三点：1）提出基于Transformer的全局跟踪方法GloTSFormer，实现多帧同步关联；2）引入基于Wasserstein距离的帧间位置关联方法；3）在公共数据集上进行大量实验。在ICDAR2015视频数据集上，GloTSFormer实现了56.0的MOTA指标，较先前最佳方法绝对值提升4.6，并显著超越此前基于Transformer的方法8.3个MOTA值。