Autor segons l'article: Abdelwahab, Mohamed A.; Abdel-Nasser, Mohamed; Hori, Maiya;
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed
Paraules clau: Videos Trajectory Traffic surveillance system Support vector machines Scale Roads Residual network Reliability Meteorology Feature extraction Deep learning Congestion
Resum: Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of vehicles. Existing systems largely depend on texture and motion features. Such systems face several challenges, including illumination changes caused by variations in weather conditions, complexity of scenes, vehicle occlusion, and the ambiguity of stopped vehicles. To overcome these issues, this article proposes a rapid and reliable traffic congestion detection method based on the modeling of video dynamics using deep residual learning and motion trajectories. The proposed method efficiently uses both motion and deep texture features to overcome the limitations of existing methods. Unlike other methods that simply extract texture features from a single frame, we use an efficient representation learning method to capture the latent structures in traffic videos by modeling the evolution of texture features. This representation yields a noticeable improvement in detection results under various weather conditions. Regarding motion features, we propose an algorithm to distinguish stopped vehicles and background objects, whereas most existing motion-based approaches fail to address this issue. Both types of obtained features are used to construct an ensemble classification model based on the support vector machine algorithm. Two benchmark datasets are considered to demonstrate the robustness of the proposed method: the UCSD dataset and NU1 video dataset. The proposed method achieves competitive results (97.64% accuracy) when compared to state-of-the-art methods.
Àrees temàtiques: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat
Identificador de l'autor: 0000-0002-1074-2441
Data d'alta del registre: 2023-05-14
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://ieeexplore.ieee.org/document/9211398
Referència a l'article segons font original: Ieee Access. 8 182180-182192
Referència de l'ítem segons les normes APA: Abdelwahab, Mohamed A.; Abdel-Nasser, Mohamed; Hori, Maiya; (2020). Reliable and Rapid Traffic Congestion Detection Approach Based on Deep Residual Learning and Motion Trajectories. Ieee Access, 8(), 182180-182192. DOI: 10.1109/ACCESS.2020.3028395
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.1109/ACCESS.2020.3028395
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications