Modeling Human Memory in Multi-Object Tracking with Transformers

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)(2022)

引用 2|浏览18
暂无评分
摘要
When tracking objects, humans rely on a memory mechanism, memorize the track of an object then look for it in the current scene. In this paper, we propose Memory-based Multi-object Tracking with Transformers (MMTT) to mimic human behavior in multi-object tracking. Unlike Re-ID-based methods, MMTT solves multi-object tracking in an explicit way, with a Track Encoder to extract track memory, a Detection Encoder to extract detection interactions, and a Memory Decoder to simulate the "look" process. The design of MMTT has the ability to model both spatial and temporal information of a single track. We evaluate on commonly used MOT datasets and the experimental results demonstrate its superior effectiveness. We hope this paper can provide a novel direction for the MOT task. The code and models will be made publicly available upon acceptance.
更多
查看译文
关键词
Multi-object tracking,Transformer,Human memory modeling,Deep learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要