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LITE-BoostTrack: A Hybrid RealTime MultiObject Tracking Architecture for Resource-Constrained Environments
Corresponding Author(s) : Adhitya Nugraha
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026 (Article in Progress)
Abstract
Multi object tracking (MOT) is a crucial component of modern computer vision applications, ranging from intelligent surveillance to autonomous vehicles. The primary challenge in MOT lies in maintaining identity consistency under conditions of high density and frequent occlusion, while also ensuring computational efficiency for real time deployment on resource constrained devices. This paper introduces LITE BoostTrack, a hybrid architecture that combines the confidence scaling based association mechanism of BoostTrack with the lightweight feature extraction strategy of the Lightweight Integrated Tracking and Embedding (LITE) framework. By leveraging internal features from the YOLOv8 detector without relying on an external Re Identification module, the proposed approach reduces computational burden while preserving robustness in identity association. Experiments were conducted on the MOT20 benchmark using standard evaluation metrics, namely HOTA, MOTA, IDF1, IDSW, and FPS, to comprehensively assess both tracking accuracy and runtime efficiency. The results demonstrate that LITE BoostTrack achieves competitive accuracy, with a HOTA of 27.32 and an IDF1 of 37.49, which are nearly equivalent to the original BoostTrack. At the same time, it delivers a substantial improvement in runtime efficiency, reaching 13.23 FPS, almost twice the speed of standard BoostTrack. These findings confirm that efficiency optimization in MOT can be achieved through architectural reengineering that exploits detector internal features without the need for additional deep modules. LITE BoostTrack therefore represents a balanced and practical solution that combines accuracy with efficiency, making it well suited for real time applications in edge computing and resource constrained environments.
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- S. Li, H. Ren, X. Xie, and Y. Cao, “A Review of Multi-Object Tracking in Recent Times,” Jan. 01, 2025, John Wiley and Sons Inc. doi: 10.1049/cvi2.70010.
- M. A. Altaf and M. Y. Kim, “Multiple object detection and tracking in autonomous vehicles: A survey on enhanced affinity computation and its multimodal applications,” Aug. 01, 2025, Korean Institute of Communications and Information Sciences. doi: 10.1016/j.icte.2025.06.005.
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- M. Elshahawy, A. O. Aseeri, S. El-Sappagh, H. Soliman, M. Elmogy, and M. Abu-Elkheir, “Identification and Classification of Crowd Activities,” Computers, Materials and Continua, vol. 72, no. 1, pp. 815–832, 2022, doi: 10.32604/cmc.2022.023852.
- J. Alikhanov, D. Obidov, M. Abdurasulov, and H. Kim, “Practical Evaluation Framework for Real-Time Multi-Object Tracking: Achieving Optimal and Realistic Performance,” IEEE Access, vol. 13, pp. 34768–34788, 2025, doi: 10.1109/ACCESS.2025.3541177.
- P. Zhang, D. Wang, and H. Lu, “Multi-modal visual tracking: Review and experimental comparison,” Apr. 01, 2024, Tsinghua University. doi: 10.1007/s41095-023-0345-5.
- S. Honarparvar, Z. B. Ashena, S. Saeedi, and S. Liang, “A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams,” Nov. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s24227238.
- L. Ye, W. Li, L. Zheng, and Y. Zeng, “Lightweight and Deep Appearance Embedding for Multiple Object Tracking,” IET Computer Vision, vol. 16, no. 6, pp. 489–503, Sep. 2022, doi: 10.1049/cvi2.12106.
- J. Yan, S. Du, and Y. Wang, “Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter,” IEEE Access, vol. 10, pp. 118512–118521, 2022, doi: 10.1109/ACCESS.2022.3220635.
- Z. Wan and W. Wu, “A robust approach to deformed pedestrian tracking with multi-trajectory prediction,” Cluster Comput, vol. 28, no. 5, Oct. 2025, doi: 10.1007/s10586-024-05059-1.
- V. M. Scarrica, C. Panariello, A. Ferone, and A. Staiano, “A hybrid approach to real-time multi-target tracking,” Jun. 01, 2024, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s00521-024-09799-4.
- P. Dendorfer et al., “MOT20: A benchmark for multi object tracking in crowded scenes,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.09003
- N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in 2017 IEEE International Conference on Image Processing (ICIP), IEEE, Sep. 2017, pp. 3645–3649. doi: 10.1109/ICIP.2017.8296962.
- Y. Du et al., “StrongSORT: Make DeepSORT Great Again,” IEEE Trans Multimedia, vol. 25, pp. 8725–8737, Feb. 2023, doi: 10.1109/TMM.2023.3240881.
- J. Cao, J. Pang, X. Weng, R. Khirodkar, and K. Kitani, “Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2023, pp. 9686–9696. doi: 10.1109/CVPR52729.2023.00934.
- G. Maggiolino, A. Ahmad, J. Cao, and K. Kitani, “Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification,” in Proceedings - International Conference on Image Processing, ICIP, IEEE Computer Society, 2023, pp. 3025–3029. doi: 10.1109/ICIP49359.2023.10222576.
- N. Aharon, R. Orfaig, and B.-Z. Bobrovsky, “BoT-SORT: Robust Associations Multi-Pedestrian Tracking,” Jul. 2022, [Online]. Available: http://arxiv.org/abs/2206.14651
- V. D. Stanojevic and B. T. Todorovic, “BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking,” Mach Vis Appl, vol. 35, no. 3, May 2024, doi: 10.1007/s00138-024-01531-5.
- J. Alikhanov, D. Obidov, and H. Kim, “LITE: A Paradigm Shift in Multi-object Tracking with Efficient ReID Feature Integration,” vol. 15293, M. Mahmud, M. Doborjeh, K. Wong, A. C. S. Leung, Z. Doborjeh, and M. Tanveer, Eds., in Lecture Notes in Computer Science, vol. 15293., Singapore: Springer Nature Singapore, 2025, pp. 92–106. doi: 10.1007/978-981-96-6596-9_7.
References
S. Li, H. Ren, X. Xie, and Y. Cao, “A Review of Multi-Object Tracking in Recent Times,” Jan. 01, 2025, John Wiley and Sons Inc. doi: 10.1049/cvi2.70010.
M. A. Altaf and M. Y. Kim, “Multiple object detection and tracking in autonomous vehicles: A survey on enhanced affinity computation and its multimodal applications,” Aug. 01, 2025, Korean Institute of Communications and Information Sciences. doi: 10.1016/j.icte.2025.06.005.
W. Shi, X. Zheng, L. Zhang, C. Ji, Y. Zhang, and J. Bian, “Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism,” Signal Processing, vol. 236, Nov. 2025, doi: 10.1016/j.sigpro.2025.110058.
M. Elshahawy, A. O. Aseeri, S. El-Sappagh, H. Soliman, M. Elmogy, and M. Abu-Elkheir, “Identification and Classification of Crowd Activities,” Computers, Materials and Continua, vol. 72, no. 1, pp. 815–832, 2022, doi: 10.32604/cmc.2022.023852.
J. Alikhanov, D. Obidov, M. Abdurasulov, and H. Kim, “Practical Evaluation Framework for Real-Time Multi-Object Tracking: Achieving Optimal and Realistic Performance,” IEEE Access, vol. 13, pp. 34768–34788, 2025, doi: 10.1109/ACCESS.2025.3541177.
P. Zhang, D. Wang, and H. Lu, “Multi-modal visual tracking: Review and experimental comparison,” Apr. 01, 2024, Tsinghua University. doi: 10.1007/s41095-023-0345-5.
S. Honarparvar, Z. B. Ashena, S. Saeedi, and S. Liang, “A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams,” Nov. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s24227238.
L. Ye, W. Li, L. Zheng, and Y. Zeng, “Lightweight and Deep Appearance Embedding for Multiple Object Tracking,” IET Computer Vision, vol. 16, no. 6, pp. 489–503, Sep. 2022, doi: 10.1049/cvi2.12106.
J. Yan, S. Du, and Y. Wang, “Multi-Pedestrian Tracking in Crowded Scenes by Modeling Movement Behavior and Optimizing Kalman Filter,” IEEE Access, vol. 10, pp. 118512–118521, 2022, doi: 10.1109/ACCESS.2022.3220635.
Z. Wan and W. Wu, “A robust approach to deformed pedestrian tracking with multi-trajectory prediction,” Cluster Comput, vol. 28, no. 5, Oct. 2025, doi: 10.1007/s10586-024-05059-1.
V. M. Scarrica, C. Panariello, A. Ferone, and A. Staiano, “A hybrid approach to real-time multi-target tracking,” Jun. 01, 2024, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s00521-024-09799-4.
P. Dendorfer et al., “MOT20: A benchmark for multi object tracking in crowded scenes,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.09003
N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in 2017 IEEE International Conference on Image Processing (ICIP), IEEE, Sep. 2017, pp. 3645–3649. doi: 10.1109/ICIP.2017.8296962.
Y. Du et al., “StrongSORT: Make DeepSORT Great Again,” IEEE Trans Multimedia, vol. 25, pp. 8725–8737, Feb. 2023, doi: 10.1109/TMM.2023.3240881.
J. Cao, J. Pang, X. Weng, R. Khirodkar, and K. Kitani, “Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2023, pp. 9686–9696. doi: 10.1109/CVPR52729.2023.00934.
G. Maggiolino, A. Ahmad, J. Cao, and K. Kitani, “Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification,” in Proceedings - International Conference on Image Processing, ICIP, IEEE Computer Society, 2023, pp. 3025–3029. doi: 10.1109/ICIP49359.2023.10222576.
N. Aharon, R. Orfaig, and B.-Z. Bobrovsky, “BoT-SORT: Robust Associations Multi-Pedestrian Tracking,” Jul. 2022, [Online]. Available: http://arxiv.org/abs/2206.14651
V. D. Stanojevic and B. T. Todorovic, “BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking,” Mach Vis Appl, vol. 35, no. 3, May 2024, doi: 10.1007/s00138-024-01531-5.
J. Alikhanov, D. Obidov, and H. Kim, “LITE: A Paradigm Shift in Multi-object Tracking with Efficient ReID Feature Integration,” vol. 15293, M. Mahmud, M. Doborjeh, K. Wong, A. C. S. Leung, Z. Doborjeh, and M. Tanveer, Eds., in Lecture Notes in Computer Science, vol. 15293., Singapore: Springer Nature Singapore, 2025, pp. 92–106. doi: 10.1007/978-981-96-6596-9_7.