Skip to content

[IEEE Access - 2022] LMOT : Efficient Light-Weight Detection and Tracking in Crowds

License

Notifications You must be signed in to change notification settings

RanaMostafaAbdElMohsen/LMOT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LMOT: Efficient Light-Weight Detection and Tracking in Crowds

This repository is for a masters research project at Cairo University, computer engineering department.

This paper introduces LMOT: Efficient Light-Weight Detection and Tracking in Crowds published in IEEE access.

LMOT: Efficient Light-Weight Detection and Tracking in Crowds,
RANA MOSTAFA, HODA BARAKA, AND ABDELMONIEM BAYOUMI

@article{LMOT,
  author={Mostafa, Rana and Baraka, Hoda and Bayoumi, AbdElmoniem},
  journal={IEEE Access}, 
  title={LMOT: Efficient Light-Weight Detection and Tracking in Crowds}, 
  year={2022},
  doi={10.1109/ACCESS.2022.3197157}
}

Contact: Rana Mostafa, AbdElMoniem Bayoumi. Any questions or discussion are welcome!

Demo

Please have a look at demo here

Abstract

Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model.

Screenshot

Main results

Alt text Alt text Alt text

Acknowledgement

The code is based on xingyizhou/CenterNet. Thanks for your wonderful works.

Releases

No releases published

Packages

No packages published