Articles producció científica> Enginyeria Informàtica i Matemàtiques

Reliable and Rapid Traffic Congestion Detection Approach Based on Deep Residual Learning and Motion Trajectories

  • Dades identificatives

    Identificador: imarina:9002969
    Autors:
    Abdelwahab, Mohamed A.Abdel-Nasser, MohamedHori, Maiya
    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.
  • Altres:

    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
  • Paraules clau:

    Computer Science (Miscellaneous),Computer Science, Information Systems,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Materials Science (Miscellaneous),Telecommunications
    Videos
    Trajectory
    Traffic surveillance system
    Support vector machines
    Scale
    Roads
    Residual network
    Reliability
    Meteorology
    Feature extraction
    Deep learning
    Congestion
    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
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