Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MAML MOT: Multiple Object Tracking based on Meta-Learning

Jiayi Chen, Chunhua Deng

TL;DR

This paper tackles the few-shot Re-ID challenge in multi-object tracking by introducing MAML MOT, a meta-learning framework that combines offline MAML-based base-learner training with online meta-learner adaptation for rapid task-specific learning. By formulating Re-ID as a set of cross-task learning problems, the method constructs tasks from pedestrian appearances and applies hierarchical inner/outer optimization to obtain task-sensitive initializations. Online tracking leverages a similarity-weighted combination of offline bases to initialize and quickly adapt to new identities, updating only the last layer to maintain speed. Empirical results on MOT16, MOT17, and MOT20 show competitive MOTA and reduced ID switches, with robust IDF1 performance, demonstrating improved generalization and robustness in pedestrian MOT. Overall, MAML MOT offers a practical meta-learning solution to enhance Re-ID in complex tracking scenarios and sets a foundation for rapid adaptation in visual tracking tasks.

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.

MAML MOT: Multiple Object Tracking based on Meta-Learning

TL;DR

This paper tackles the few-shot Re-ID challenge in multi-object tracking by introducing MAML MOT, a meta-learning framework that combines offline MAML-based base-learner training with online meta-learner adaptation for rapid task-specific learning. By formulating Re-ID as a set of cross-task learning problems, the method constructs tasks from pedestrian appearances and applies hierarchical inner/outer optimization to obtain task-sensitive initializations. Online tracking leverages a similarity-weighted combination of offline bases to initialize and quickly adapt to new identities, updating only the last layer to maintain speed. Empirical results on MOT16, MOT17, and MOT20 show competitive MOTA and reduced ID switches, with robust IDF1 performance, demonstrating improved generalization and robustness in pedestrian MOT. Overall, MAML MOT offers a practical meta-learning solution to enhance Re-ID in complex tracking scenarios and sets a foundation for rapid adaptation in visual tracking tasks.

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.
Paper Structure (10 sections, 9 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 10 sections, 9 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. MAML MOT
  3. OVERVIEW
  4. Offline training method of base learners based on MAML
  5. Online learning tracking strategy of meta-learner
  6. EXPERIMENT
  7. Dataset and Evaluation Metrics
  8. Implementation details
  9. Analysis of Results
  10. CONCLUSIONS

Figures (4)

  • Figure 1: (a) The red box indicates the pedestrian detected during the pedestrian detection task. (b) Boxes of the same color represent the same pedestrian ID, while boxes of different colors represent different pedestrian IDs.
  • Figure 2: Overview of MAML MOT.
  • Figure 3: Multi-object tracking task sample reorganization.
  • Figure 4: Meta-learning hierarchical optimization process.