Tracking a time-varying indefinite number of objects in a video sequence over
time remains a challenge despite recent advances in the field. Most existing
approaches are not able to properly handle multi-object tracking challenges
such as occlusion, in part because they ignore long-term temporal information.
To address these shortcomings, we present MO3TR: a truly end-to-end
Transformer-based online multi-object tracking (MOT) framework that learns to
handle occlusions, track initiation and termination without the need for an
explicit data association module or any heuristics. MO3TR encodes object
interactions into long-term temporal embeddings using a combination of spatial
and temporal Transformers, and recursively uses the information jointly with
the input data to estimate the states of all tracked objects over time. The
spatial attention mechanism enables our framework to learn implicit
representations between all the objects and the objects to the measurements,
while the temporal attention mechanism focuses on specific parts of past
information, allowing our approach to resolve occlusions over multiple frames.
Our experiments demonstrate the potential of this new approach, achieving
results on par with or better than the current state-of-the-art on multiple MOT
metrics for several popular multi-object tracking benchmarks.

使用 Transformer 和时空嵌入的 MO3TR 方法，具有空间和时间注意机制，无需显式的数据聚类模块或任何启发式方法，成功解决了多物体追踪过程中的诸多挑战，表现方面同多个 popular MOT 图像和视频基准测试的现有最先进技术几乎相当或更好。