The tracking-by-detection paradigm today has become the dominant method for
multi-object tracking and works by detecting objects in each frame and then
performing data association across frames. However, its sequential frame-wise
matching property fundamentally suffers from the intermediate interruptions in
a video, such as object occlusions, fast camera movements, and abrupt light
changes. Moreover, it typically overlooks temporal information beyond the two
frames for matching. In this paper, we investigate an alternative by treating
object association as clip-wise matching. Our new perspective views a single
long video sequence as multiple short clips, and then the tracking is performed
both within and between the clips. The benefits of this new approach are two
folds. First, our method is robust to tracking error accumulation or
propagation, as the video chunking allows bypassing the interrupted frames, and
the short clip tracking avoids the conventional error-prone long-term track
memory management. Second, the multiple frame information is aggregated during
the clip-wise matching, resulting in a more accurate long-range track
association than the current frame-wise matching. Given the state-of-the-art
tracking-by-detection tracker, QDTrack, we showcase how the tracking
performance improves with our new tracking formulation. We evaluate our
proposals on two tracking benchmarks, TAO and MOT17 that have complementary
characteristics and challenges each other.

通过将单个视频序列视为多个短片段的方法，提出了一种新的目标关联方法，避免了中间中断的影响，聚合多帧信息实现更准确的长程跟踪关联，并在 TAO 和 MOT17 两个跟踪基准测试中进行了评估。