Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach

Pranamesh Chakraborty, Anuj Sharma, Chinmay Hegde

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Early detection of incidents is a key step to reduce incident related congestion. State Department of Transportation (DoTs) usually install a large number of Close Circuit Television (CCTV) cameras in freeways for traffic surveillance. In this study, we used semi-supervised techniques to detect traffic incident trajectories from the cameras. Vehicle trajectories are identified from the cameras using state-of-the-art deep learning based You Look Only Once (YOLOv3) classifier and Simple Online Realtime Tracking (SORT) is used for vehicle tracking. Our proposed approach for trajectory classification is based on semi-supervised parameter estimation using maximum-likelihood (ML) estimation. The ML based Contrastive Pessimistic Likelihood Estimation (CPLE) attempts to identify incident trajectories from the normal trajectories. We compared the performance of CPLE algorithm to traditional semi-supervised techniques Self Learning and Label Spreading, and also to the classification based on the corresponding supervised algorithm. Results show that approximately 14% improvement in trajectory classification can be achieved using the proposed approach.

    Original languageEnglish (US)
    Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1840-1845
    Number of pages6
    ISBN (Electronic)9781728103235
    DOIs
    StatePublished - Dec 7 2018
    Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
    Duration: Nov 4 2018Nov 7 2018

    Publication series

    NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
    Volume2018-November

    Conference

    Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
    CountryUnited States
    CityMaui
    Period11/4/1811/7/18

    Keywords

    • maximum likelihood estimation
    • semi-supervised learning
    • surveillance
    • traffic incident detection

    ASJC Scopus subject areas

    • Automotive Engineering
    • Mechanical Engineering
    • Computer Science Applications

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  • Cite this

    Chakraborty, P., Sharma, A., & Hegde, C. (2018). Freeway Traffic Incident Detection from Cameras: A Semi-Supervised Learning Approach. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018 (pp. 1840-1845). [8569426] (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2018.8569426