التفاصيل البيبلوغرافية
| العنوان: |
LMOT: Efficient Light-Weight Detection and Tracking in Crowds |
| المؤلفون: |
Rana Mostafa, Hoda Baraka, Abdelmoniem Bayoumi |
| المصدر: |
IEEE Access, Vol 10, Pp 83085-83095 (2022) |
| بيانات النشر: |
IEEE, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Multi-object tracking, pedestrian tracking, joint detection and tracking, object detection, deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9852199/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3197157 |
| URL الوصول: |
https://doaj.org/article/55277569243046348e990e4d951b6083 |
| رقم الانضمام: |
edsdoj.55277569243046348e990e4d951b6083 |
| قاعدة البيانات: |
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