  Multi-object tracking (MOT) aims to associate target objects across video
frames in order to obtain entire moving trajectories. With the advancement of
deep neural networks and the increasing demand for intelligent video analysis,
MOT has gained significantly increased interest in the computer vision
community. Embedding methods play an essential role in object location
estimation and temporal identity association in MOT. Unlike other computer
vision tasks, such as image classification, object detection,
re-identification, and segmentation, embedding methods in MOT have large
variations, and they have never been systematically analyzed and summarized. In
this survey, we first conduct a comprehensive overview with in-depth analysis
for embedding methods in MOT from seven different perspectives, including
patch-level embedding, single-frame embedding, cross-frame joint embedding,
correlation embedding, sequential embedding, tracklet embedding, and
cross-track relational embedding. We further summarize the existing widely used
MOT datasets and analyze the advantages of existing state-of-the-art methods
according to their embedding strategies. Finally, some critical yet
under-investigated areas and future research directions are discussed.
