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Real-Time Detection of Traffic Anomalies Near Roundabouts

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Pattern Recognition and Artificial Intelligence (MedPRAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1543))

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Abstract

Visual analytics can bridge the gap between computational and human approaches for detecting traffic anomalies near the round-about, making the data analysis process more transparent. The main problem with anomaly detection is the unavailability of anomalous data as they do not occur frequently. Moreover, there is a variety in the characteristics of the same real-time scenario. A solution is proposed to handle these issues using unsupervised learning which mainly encloses the detection of vehicles, road, roundabout violations, and traffic jams near the round-about. Initially, vehicles, roads, and round-about are detected based on Histogram of Gradient (HoG) feature descriptor and color information. Then, optical flow is applied to the detected vehicles for obtaining their trajectory information. Finally, the round-about violation is detected based on the position and angle between the centre of the round-about and the vehicle trajectory. Additionally, the number of vehicles near the round-about is computed for detecting the traffic jam. The effectiveness of the proposed algorithm is demonstrated over 30 real-time traffic videos. Some additional comparative studies are done over the benchmark data YouTube8M. Through an extensive study, it can be concluded that our proposed algorithm is superior to some state-of-the-arts.

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Acknowledgement

The work is done under UAY project, Government of India (GOI), funded by Ministry of Education & Ministry of Steel (GOI), and Tata Steel Limited. We acknowledge the Centre of Excellence in Safety Engineering and Analytics (CoE-SEA) (www.iitkgp.ac.in/department/SE), IIT Kharagpur and Safety Analytics & Virtual Reality (SAVR) Laboratory (www.savr.iitkgp.ac.in) of Department of Industrial & Systems Engineering, IIT Kharagpur for experimental/computational and research facilities for this work.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, IIT Kharagpur, Kharagpur, 721302, India

    Anima Pramanik & J. Maiti

  2. Information Systems and Business Analytics, IIM Ranchi, Ranchi, 834008, Jharkhand, India

    Sobhan Sarkar

  3. Department of Mathematics and Computer Science, Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  4. LITIS Lab, University of Rouen, Rouen, France

    Chawki Djeddi

  5. Centre of Excellence on Safety Engineering and Analytics, IIT Kharagpur, Kharagpur, 721302, India

    J. Maiti

Authors
  1. Anima Pramanik
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  2. Sobhan Sarkar
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  4. J. Maiti
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Corresponding author

Correspondence to Anima Pramanik .

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Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  3. FAST National University, Islamabad, Pakistan

    Akhtar Jamil

  4. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Alaa Ali Hameed

  5. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    İsmail Kucuk

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Pramanik, A., Sarkar, S., Djeddi, C., Maiti, J. (2022). Real-Time Detection of Traffic Anomalies Near Roundabouts. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-04112-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04111-2

  • Online ISBN: 978-3-031-04112-9

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