MAML MOT: Multiple Object Tracking based on Meta-Learning

TL;DR

This paper introduces MAML MOT, a meta-learning approach to improve pedestrian re-identification in multi-object tracking by addressing sample scarcity and enhancing model robustness, achieving high accuracy on standard datasets.

Contribution

The paper presents a novel meta-learning-based training method for multi-object tracking that enhances re-identification performance under limited sample conditions.

Findings

Achieves high accuracy on mainstream MOT datasets.

Effectively addresses sample scarcity in re-identification tasks.

Improves model robustness and generalization in complex scenes.

Abstract

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.