Enhancing Abandoned Object Detection with Dual Background Models and Yolo-NAS


  • Saluky Electrical and Informatics Engineering, ITB, Indonesia.
  • Gusti Baskara Nugraha Electrical and Informatics Engineering, ITB, Indonesia.
  • Suhono Harso Supangkat Electrical and Informatics Engineering, ITB, Indonesia.


Dual background model, YOLO-NAS, Abandoned Object


The rapid advancement of computer vision technology has paved the way for many surveillance, security, and public safety applications. Abandoned object detection, a critical component in video surveillance systems, plays an essential role in identifying potential security threats and ensuring the safety of public spaces. This paper proposes a new approach for abandoned object detection that combines a dual background model with YOLO-NAS (You Only Look Once Neural Architecture Search), a state-of-the-art object detection framework. The proposed method utilizes two background models with different learning rates, one based on fast background subtraction and one using slower background modeling. The dual background model is verified with YOLO-NAS to improve the accuracy and robustness of abandoned object detection. This model can perform better in low light conditions and changes in illumination. Incorporating YOLO-NAS into the framework enables real-time object detection and tracking, enabling efficient and accurate identification of abandoned objects. YOLO-NAS improves detection speed and maintains high precision, making it an ideal candidate for real-time surveillance applications. Our experimental results, conducted on diverse video sequences, demonstrate the superiority of the proposed approach over existing methods. The dual background model combined with YOLO-NAS consistently outperforms other abandoned object detection algorithms regarding accuracy and speed. The proposed method is robust in challenging scenarios, including dense environments and varying lighting conditions. This paper presents a new abandoned object detection system that leverages the dual background model and YOLO-NAS to achieve state-of-the-art accuracy, speed, and robustness performance. The proposed approach promises to improve security and surveillance systems in public spaces, transportation hubs, and critical infrastructure, thereby contributing to increased public safety and threat prevention.


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How to Cite

Saluky, S., Nugraha, G. B. ., & Supangkat, S. H. . (2023). Enhancing Abandoned Object Detection with Dual Background Models and Yolo-NAS. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 547–554. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4298



Research Article