Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining

Chongke Wu, Sicong Shao, Cihan Tunc, Salim Hariri

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

1 Scopus citations

Abstract

Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.

Original languageEnglish (US)
Title of host publication2020 IEEE/ACS 17th International Conference on Computer Systems and Applications, AICCSA 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728185774
DOIs
StatePublished - Nov 2020
Event17th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2020 - Virtual, Antalya, Turkey
Duration: Nov 2 2020Nov 5 2020

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2020-November
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference17th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2020
CountryTurkey
CityVirtual, Antalya
Period11/2/2011/5/20

Keywords

  • Security
  • abnormal event detection
  • anomaly video analysis
  • context mining
  • deep features
  • video surveillance

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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