Papers › LMOT: Efficient Light-Weight Detection and Tracking in Crowds
LMOT: Efficient Light-Weight Detection and Tracking in Crowds
Rana Mostafa, Hoda Baraka, AbdElMoniem Bayoumi
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Object Tracking | MOT16 | LMOT | IDF1 | 72.3 | #8 of 24 | Archive leaderboard | report |
| Multi-Object Tracking | MOT16 | LMOT | IDs | 669 | #8 of 24 | Archive leaderboard | report |
| Multi-Object Tracking | MOT16 | LMOT | MOTA | 73.2 | #8 of 24 | Archive leaderboard | report |
| Multi-Object Tracking | MOT17 | LMOT | IDF1 | 70.3 | #33 of 48 | Archive leaderboard | report |
| Multi-Object Tracking | MOT17 | LMOT | MOTA | 72.0 | #33 of 48 | Archive leaderboard | report |
| Multi-Object Tracking | MOT20 | LMOT | IDF1 | 61.1 | #25 of 27 | Archive leaderboard | report |
| Multi-Object Tracking | MOT20 | LMOT | MOTA | 59.1 | #25 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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