  In Multiple Object Tracking (MOT), tracking-by-detection methods have stood
the test for a long time, which split the process into two parts according to
the definition: object detection and association. They leverage robust
single-frame detectors and treat object association as a post-processing step
through hand-crafted heuristic algorithms and surrogate tasks. However, the
nature of heuristic techniques prevents end-to-end exploitation of training
data, leading to increasingly cumbersome and challenging manual modification
while facing complicated or novel scenarios. In this paper, we regard this
object association task as an End-to-End in-context ID prediction problem and
propose a streamlined baseline called MOTIP. Specifically, we form the target
embeddings into historical trajectory information while considering the
corresponding IDs as in-context prompts, then directly predict the ID labels
for the objects in the current frame. Thanks to this end-to-end process, MOTIP
can learn tracking capabilities straight from training data, freeing itself
from burdensome hand-crafted algorithms. Without bells and whistles, our method
achieves impressive state-of-the-art performance in complex scenarios like
DanceTrack and SportsMOT, and it performs competitively with other
transformer-based methods on MOT17. We believe that MOTIP demonstrates
remarkable potential and can serve as a starting point for future research. The
code is available at https://github.com/MCG-NJU/MOTIP.
