Real-time camera tampering detection using two-stage scene matching

Chao Ching Shih, Shen Chi Chen, Cheng Feng Hung, Kuan-Wen Chen, Shih Yao Lin, Chih Wei Lin, Yi Ping Hung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

We propose a tampering detection method using two-stage scene matching for real application with high efficiency and low false alarm rate. In the first stage, we use the intensity of edges as the main cue to detect the camera tampering events. Instead of using the entire edge points of the images, we sample the most significant edge points to represent the scene. Analyzing the edge variation with only the sample points, we discover that the events of camera tampering can be detected with low computation cost. Whenever the first stage detects the tampering event, the second stage is triggered to reduce false alarms. In the second stage, we propose an illumination change detector which can check the consistency of the scene structure using cell-based matching method. The experimental results demonstrate that our system can detect the camera tampering precisely and minimize false alarm even when the illumination changes dramatically or large crowds passing through the scene.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
StatePublished - 21 Oct 2013
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: 15 Jul 201319 Jul 2013

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2013 IEEE International Conference on Multimedia and Expo, ICME 2013
CountryUnited States
CitySan Jose, CA
Period15/07/1319/07/13

Keywords

  • illumination change detector
  • tampering detection
  • two-stage scene matching

Fingerprint Dive into the research topics of 'Real-time camera tampering detection using two-stage scene matching'. Together they form a unique fingerprint.

Cite this