Current multi-category Multiple Object Tracking (MOT) metrics use class labels to group tracking results for per-class evaluation. Similarly, MOT methods typically only associate objects with the same class predictions. These two prevalent strategies in MOT implicitly assume that the classification performance is near-perfect. However, this is far from the case in recent large-scale MOT datasets, which contain large numbers of classes with many rare or semantically similar categories. Therefore, the resulting inaccurate classification leads to sub-optimal tracking and inadequate benchmarking of trackers. We address these issues by disentangling classification from tracking. We introduce a new metric, Track Every Thing Accuracy (TETA), breaking tracking measurement into three sub-factors: localization, association, and classification, allowing comprehensive benchmarking of tracking performance even under inaccurate classification. TETA also deals with the challenging incomplete annotation problem in large-scale tracking datasets. We further introduce a Track Every Thing tracker (TETer), that performs association using Class Exemplar Matching (CEM). Our experiments show that TETA evaluates trackers more comprehensively, and TETer achieves significant improvements on the challenging large-scale datasets BDD100K and TAO compared to the state-of-the-art.
翻译:目前多类多物体跟踪(MOT)指标使用分类标签来对每类评估进行分组跟踪结果。同样,MOT方法通常只将对象与同一类预测联系起来。MOT的这两个普遍战略暗含地假定分类性能接近完美。然而,与最近的大型MOT数据集的情况相去甚远,这些数据集包含大量类别,有许多稀有或语义相似的类别。因此,由此造成的分类不准确导致跟踪者次最佳跟踪和基准设定不足。我们通过分解跟踪分类来解决这些问题。我们引入了新的指标,跟踪每类准确性(TETA),将跟踪测量破碎到三个子要素:本地化、关联和分类,允许在不准确分类的情况下对跟踪性能进行全面基准。TETA还处理大规模跟踪数据集中具有挑战性的不完整说明问题。我们进一步引入了每个跟踪跟踪器(TETer),该跟踪器使用分类测试匹配(CEM)来进行关联。我们的实验显示,TETA的跟踪器将更具挑战性的大规模跟踪器与BATDS进行对比。