Object tracking (OT) aims to estimate the positions of target objects in a video sequence. Depending on whether the initial states of target objects are specified by provided annotations in the first frame or the categories, OT could be classified as instance tracking (e.g., SOT and VOS) and category tracking (e.g., MOT, MOTS, and VIS) tasks. Combing the advantages of the best practices developed in both communities, we propose a novel tracking-with-detection paradigm, where tracking supplements appearance priors for detection and detection provides tracking with candidate bounding boxes for association. Equipped with such a design, a unified tracking model, OmniTracker, is further presented to resolve all the tracking tasks with a fully shared network architecture, model weights, and inference pipeline. Extensive experiments on 7 tracking datasets, including LaSOT, TrackingNet, DAVIS16-17, MOT17, MOTS20, and YTVIS19, demonstrate that OmniTracker achieves on-par or even better results than both task-specific and unified tracking models.

翻译：目标跟踪（OT）旨在估计视频序列中目标对象的位置。根据目标对象的初始状态由第一帧提供的标注还是类别指定，OT可分为实例跟踪（如SOT和VOS）和类别跟踪（如MOT、MOTS和VIS）任务。我们结合两个领域最佳实践的优势，提出了一种新型的跟踪-检测联合范式：跟踪为检测补充外观先验，检测为跟踪提供候选边界框以供关联。基于此设计，我们进一步提出了统一跟踪模型OmniTracker，该模型通过完全共享的网络架构、模型权重和推理流程解决所有跟踪任务。在7个跟踪数据集（包括LaSOT、TrackingNet、DAVIS16-17、MOT17、MOTS20和YTVIS19）上的大量实验表明，OmniTracker取得了与特定任务模型和统一跟踪模型相当甚至更优的结果。