来自今天的爱可可AI前沿推介

[CV] Beyond SOT: It's Time to Track Multiple Generic Objects at Once

C Mayer, M Danelljan, M Yang, V Ferrari, L V Gool, A Kuznetsova
[Google Research & ETH Zurich]

超越SOT: 一次跟踪多个通用目标

要点:

  1. 引入一个新的大规模GOT基准LaGOT，每个序列包含多个标注目标对象；
  2. 提出一种基于Transformer的GOT追踪器TaMOS，通过共享计算联合追踪多个对象；
  3. TaMOs在新基准上优于现有的单目标追踪器，并在单目标GOT数据集上取得非常有竞争力的结果。

摘要：
通用目标跟踪(GOT)是追踪目标对象的问题，由视频第一帧中的边框指定。虽然这项任务在过去几十年备受关注，但研究人员几乎完全专注于单个目标设置。多目标GOT受益于更广泛的适用性，使其在现实世界的应用中更具吸引力。本文对这个问题缺乏研究兴趣归因于缺乏合适的基准。本文引入一个新的大规模GOT基准LaGOT，每个序列包含多个带标注的目标对象。该基准允许研究人员应对GOT中剩余的关键挑战，旨在通过同时联合追踪多个目标来提高鲁棒性并减少计算。此外，本文提出了一种基于Transformer的GOT跟踪器TaMOS，通过共享计算联合处理多个目标。与独立追踪每个目标相比，TaMO在10个并发目标的情况下实现了4倍快的运行时，并优于新基准上现有的单目标跟踪器。最后，TaMOs在单，目标GOT数据集上取得了极具竞争力的结果，在TrackingNet上达到了新的最先进AUC成功率为84.4%。

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 researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs 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. Finally, TaMOs 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.

论文链接：https://arxiv.org/abs/2212.11920
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