  With the advancement of video analysis technology, the multi-object tracking
(MOT) problem in complex scenes involving pedestrians is gaining increasing
importance. This challenge primarily involves two key tasks: pedestrian
detection and re-identification. While significant progress has been achieved
in pedestrian detection tasks in recent years, enhancing the effectiveness of
re-identification tasks remains a persistent challenge. This difficulty arises
from the large total number of pedestrian samples in multi-object tracking
datasets and the scarcity of individual instance samples. Motivated by recent
rapid advancements in meta-learning techniques, we introduce MAML MOT, a
meta-learning-based training approach for multi-object tracking. This approach
leverages the rapid learning capability of meta-learning to tackle the issue of
sample scarcity in pedestrian re-identification tasks, aiming to improve the
model's generalization performance and robustness. Experimental results
demonstrate that the proposed method achieves high accuracy on mainstream
datasets in the MOT Challenge. This offers new perspectives and solutions for
research in the field of pedestrian multi-object tracking.
