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Video Anomaly Detection for Pedestrian Surveillance

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

With the increase in video surveillance technology, modern human beings have more viable options to enhance safety, security, and monitoring. Automatic video surveillance is an option that provides remote monitoring with little human effort and is a computer vision task. There is no end to the applications of automatic video surveillance such as traffic monitoring, theft detection, fight detection. These are important in various places like industrial, residential and official buildings, roads, and many more. The key objective of the present study is to monitor the pedestrian streets and to provide safety and security by identifying anomalous events. However, tracking an anomalous event in itself is a tricky task because of changes in the definition of an anomaly in different scenarios. In this research, high-level features are used to enhance anomaly detection performance using an auto-encoder model. The features are derived from the pre-trained models, and the contextual properties are derived from the extracted features. The datasets used for anomaly detection on the pedestrian streets are UCSD Pedestrian Street Peds1 and Peds2. The performance is evaluated on the Receiver Operating Characteristic (ROC) curve, Area under Curve (AUC), Precision-Recall curve, Average Precision, and Equal Error Rate (EER) value.

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Authors and Affiliations

  1. Computer Science and Engineering, NIT, Hamirpur, Himachal Pradesh, 177005, India

    Divakar Yadav, Saumya Asati & Arun Kumar Yadav

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201304, India

    Arti Jain

Authors
  1. Divakar Yadav
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  2. Arti Jain
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  3. Saumya Asati
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  4. Arun Kumar Yadav
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Corresponding author

Correspondence to Divakar Yadav .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Yadav, D., Jain, A., Asati, S., Yadav, A.K. (2023). Video Anomaly Detection for Pedestrian Surveillance. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_39

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