Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows users to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. In addition, we propose a transformer-based GOT tracker baseline capable of joint processing of multiple objects through shared computation. Our approach achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. In addition, our approach achieves highly competitive results on single-object GOT datasets, setting a new state of the art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.

翻译：通用目标跟踪（GOT）旨在跟踪视频首帧中通过边界框指定的目标对象。尽管该任务在过去数十年间受到广泛关注，但研究者几乎仅聚焦于单目标场景。多目标GOT具有更广泛的应用前景，在现实场景中更具吸引力。我们将该问题缺乏研究兴趣归因于缺少合适的基准数据集。本文提出全新大规模GOT基准数据集LaGOT，每个序列包含多个标注目标对象。该基准数据集致力于解决GOT中剩余的关键挑战，通过多目标联合跟踪提升鲁棒性并降低计算量。此外，我们提出基于Transformer的GOT跟踪基线模型，能够通过共享计算实现多目标联合处理。在同时跟踪10个目标时，该方法相较独立跟踪每个对象实现4倍加速，并在新基准数据集上超越现有单目标跟踪器。同时，本方法在单目标GOT数据集上取得极具竞争力的结果，在TrackingNet数据集上以84.4%的成功率AUC创下新最优记录。我们将公开基准数据集、代码及预训练模型。