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Detecting Number of Passengers in a Moving Vehicle with Publicly Available Data

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 295))

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Abstract

Detecting and counting people inside vehicles with little to no human input has many applications today. From assisting rescue authorities to smart transportation systems; from automatic crash response to law enforcement of High Occupancy Vehicle and High Occupancy Toll lanes. We propose a framework for counting passengers in moving vehicles based on various image features from various objects and contextual information. Each feature can be computed with state-of-the-art techniques, like Fisher Vectors, before being consolidated for a final detection score. Images from publicly available surveillance road cameras were taken to create a real-world data set, before training the convolutional neural network YOLOv3. Preliminary results show good prospective for this approach with the potential for improvements in each object detected in scene, thus improving the overall results. Future work can explore image enhancement with generative adversarial networks.

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Acknowledgments

This work is funded by Deep Learning Based Intrusion Detection Approaches for Advanced Traffic Management Systems, Data Science Institute, University of Houston, U.S.A.

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Houston, Houston, USA

    Luciano Branco

  2. Innovative Transportation Research Institute, Texas Southern University, Houston, USA

    Fengxiang Qiao

  3. Information and Logistics Technology, University of Houston, Houston, USA

    Yunpeng Zhang

Authors
  1. Luciano Branco
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  2. Fengxiang Qiao
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  3. Yunpeng Zhang
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Corresponding author

Correspondence to Yunpeng Zhang .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Branco, L., Qiao, F., Zhang, Y. (2022). Detecting Number of Passengers in a Moving Vehicle with Publicly Available Data. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_39

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