Recently, both long-tailed recognition and object tracking have made great advances individually. TAO benchmark presented a mixture of the two, long-tailed object tracking, in order to further reflect the aspect of the real-world. To date, existing solutions have adopted detectors showing robustness in long-tailed distributions, which derive per-frame results. Then, they used tracking algorithms that combine the temporally independent detections to finalize tracklets. However, as the approaches did not take temporal changes in scenes into account, inconsistent classification results in videos led to low overall performance. In this paper, we present a set classifier that improves accuracy of classifying tracklets by aggregating information from multiple viewpoints contained in a tracklet. To cope with sparse annotations in videos, we further propose augmentation of tracklets that can maximize data efficiency. The set classifier is plug-and-playable to existing object trackers, and highly improves the performance of long-tailed object tracking. By simply attaching our method to QDTrack on top of ResNet-101, we achieve the new state-of-the-art, 19.9% and 15.7% TrackAP_50 on TAO validation and test sets, respectively.

本文介绍了一种对长尾目标跟踪性能进行提升的方法，该方法通过聚合跟踪数据中多种视角提供的信息来改进分类器的准确性，进而处理视频场景中的不一致分类结果，同时提供数据增强工具来优化视频中稀疏注释的效率，该方法是可插拔的，并可以简单地添加到现有的物体跟踪器中，达到了新的最先进的结果。

众人拾柴火焰高：聚合多视角以更好地分类视频中的对象