  Multi-object tracking (MOT) in computer vision remains a significant
challenge, requiring precise localization and continuous tracking of multiple
objects in video sequences. The emergence of data sets that emphasize robust
reidentification, such as DanceTrack, has highlighted the need for effective
solutions. While memory-based approaches have shown promise, they often suffer
from high computational complexity and memory usage due to storing feature at
every single frame. In this paper, we propose a novel memory-based approach
that selectively stores critical features based on object motion and
overlapping awareness, aiming to enhance efficiency while minimizing
redundancy. As a result, our method not only store longer temporal information
with limited number of stored features in the memory, but also diversify states
of a particular object to enhance the association performance. Our approach
significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a
gain of 2.0% AssA score and 2.1% in IDF1 score.
