Related papers: MeMOT: Multi-Object Tracking with Memory

MeMOT: Multi-Object Tracking with Memory

URL: http://arxiv.org/abs/2203.16761v1
Date: Thu, 31 Mar 2022 02:33:20 GMT
Title: MeMOT: Multi-Object Tracking with Memory
Authors: Jiarui Cai, Mingze Xu, Wei Li, Yuanjun Xiong, Wei Xia, Zhuowen Tu, Stefano Soatto
Abstract summary: Our model, called MeMOT, consists of three main modules that are all Transformer-based. MeMOT observes very competitive performance on widely adopted MOT datasets.
Score: 97.48960039220823
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span. This is realized by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects, and by adaptively referencing and aggregating useful information from the memory as needed. Our model, called MeMOT, consists of three main modules that are all Transformer-based: 1) Hypothesis Generation that produce object proposals in the current video frame; 2) Memory Encoding that extracts the core information from the memory for each tracked object; and 3) Memory Decoding that solves the object detection and data association tasks simultaneously for multi-object tracking. When evaluated on widely adopted MOT benchmark datasets, MeMOT observes very competitive performance.

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