Towards Real-Time Multi-Object Tracking

Code

• github.com/Zhongdao/Towards-Realtime-MOT
  OfficialIn paperpytorch★ 2,443
• github.com/PaddlePaddle/PaddleDetection
  paddle★ 14,132
• github.com/JunweiLiang/Object_Detection_Tracking
  tf★ 524
• github.com/ZhangwenguangHikvision/YoloV5_JDE_TensorRT_for_Track
  none★ 55
• github.com/scuShaker/jde
  pytorch★ 0
• github.com/nemonameless/jde
  pytorch★ 0
• github.com/LKLQQ/JDE
  mindspore★ 0
• github.com/jh7cha/Towards-Real-Time-MOT
  pytorch★ 0
• github.com/nvlong21/person_Counting
  pytorch★ 0
• github.com/chandanrao007/https-github.com-Zhongdao-Towards-Realtime-MOT
  pytorch★ 0

Abstract

Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning (64.4\% MOTA 66.1\% MOTA on MOT-16 challenge). Code and models are available at https://github.com/Zhongdao/Towards-Realtime-MOT.

Tasks

Benchmark Results

Reproductions