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!
Please have a look at demo here
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.
The code is based on xingyizhou/CenterNet. Thanks for your wonderful works.