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A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings

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Abstract

This paper presents a deep learning-based framework to detect and localize the pedestrians’ anomaly behaviors in videos captured at the grade crossing. A skeleton detection and tracking algorithm are employed to capture the key point trajectories of body movements of the pedestrians. A deep recurrent neural network is applied to learn the normal patterns of pedestrians’ movements using dynamics skeleton trajectories features. An anomaly behaviors detection and localization algorithm are developed by analyzing each pedestrian’s reconstructed trajectories. In the experiments, a video dataset involving normal pedestrian behaviors is established by collecting data at multiple grade crossing spots with different camera angles. Then the proposed framework is trained on the dataset to learn the regularity patterns of normal pedestrians and localize the anomaly behaviors during the testing phase. To the best of our knowledge, it is the first attempt to analyze pedestrians’ behavior at a grade crossing. The experimental results show that the proposed framework can detect and localize the anomaly behaviors, such as squatting down, lingering, and other behaviors that may cause safety issues at the grade crossing. Our study also points out the direction for further improvement of the present development to meet the need for real-world applications.

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Acknowledgements

This research is partially funded by the Federal Railroad Administration (FRA), Contract No. 693JJ620C000021. Dr. Shala Blue, Mr. Francesco Bedini, Mr. Michael Jones, and Dr. Starr Kidda from FRA have provided essential guidance and insight. The City of Columbia, especially the Columbia Fire Department, Department of Transportation, and 911 Dispatching Center; and CSX have provided tremendous help. The opinions expressed in this article are solely those of the authors and do not represent the opinions of the funding agencies. Mr. Thomas Johnson and Mr. Tianqi Huang made significant contribution to imagery data generation.

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Authors and Affiliations

  1. NEC Laboratories America, Inc., 4 Independence Way, Princeton, NJ, 08540, USA

    Zhuocheng Jiang

  2. Department of Mechanical Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Zhuocheng Jiang, Ge Song & Yi Wang

  3. Department of Civil and Environment Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Yu Qian

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  1. Zhuocheng Jiang
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  2. Ge Song
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Correspondence to Yi Wang.

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Corresponding author: Yi Wang. Note: This work was performed at University of South Carolina when Zhuocheng Jiang worked there as a postdoc.

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Jiang, Z., Song, G., Qian, Y. et al. A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings. Neural Comput & Applic 34, 22099–22113 (2022). https://doi.org/10.1007/s00521-022-07660-0

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  • DOI: https://doi.org/10.1007/s00521-022-07660-0

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