Anomaly detection in traffic surveillance videos with GAN-based future frame prediction

Khac Tuan Nguyen, Dat Thanh Dinh, Minh N. Do, Minh Triet Tran

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

Abstract

It is essential to develop efficient methods to detect abnormal events, such as car-crashes or stalled vehicles, from surveillance cameras to provide in-time help. This motivates us to propose a novel method to detect traffic accidents in traffic videos. To tackle the problem where anomalies only occupy a small amount of data, we propose a semi-supervised method using Generative Adversarial Network trained on regular sequences to predict future frames. Our key idea is to model the ordinary world with a generative model, then compare a predicted frame with the real next frame to determine if an abnormal event occurs. We also propose a new idea of encoding motion descriptors and scaled intensity loss function to optimize GAN for fast-moving objects. Experiments on the Traffic Anomaly Detection dataset of AI City Challenge 2019 show that our method achieves the top 3 results with F1 score 0.9412 and RMSE 4.8088, and S3 score 0.9261. Our method can be applied to different related applications of anomaly and outlier detection in videos.

Original languageEnglish (US)
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages457-463
Number of pages7
ISBN (Electronic)9781450370875
DOIs
StatePublished - Jun 8 2020
Event10th ACM International Conference on Multimedia Retrieval, ICMR 2020 - Dublin, Ireland
Duration: Jun 8 2020Jun 11 2020

Publication series

NameICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval

Conference

Conference10th ACM International Conference on Multimedia Retrieval, ICMR 2020
CountryIreland
CityDublin
Period6/8/206/11/20

Keywords

  • Anomaly detection
  • Surveillance
  • Traffic
  • U-net
  • Video prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Nguyen, K. T., Dinh, D. T., Do, M. N., & Tran, M. T. (2020). Anomaly detection in traffic surveillance videos with GAN-based future frame prediction. In ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 457-463). (ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3372278.3390701