LMOT: Efficient Light-Weight Detection and Tracking in Crowds

Rana Mostafa, Hoda Baraka and AbdelMoniem Bayoumi

LMOT, i.e., Light-weight Multi-Object Tracker, 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: https://github.com/RanaMostafaAbdElMohsen/LMOT Paper: https://doi.org/10.1109/ACCESS.2022.3197157