Crowd behavior understanding through SIOF feature analysis

Li Lu, Jia He, Zhijie Xu, Yuanping Xu, Chaolong Zhang, Jing Wang, Jianhua Adu

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

3 Citations (Scopus)

Abstract

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies.

Original languageEnglish
Title of host publicationICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
Subtitle of host publicationAddressing Global Challenges through Automation and Computing
EditorsJie Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780701702618
DOIs
Publication statusPublished - 23 Oct 2017
Externally publishedYes
Event23rd IEEE International Conference on Automation and Computing, ICAC 2017 - Huddersfield, United Kingdom
Duration: 7 Sept 20178 Sept 2017

Publication series

NameICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing

Conference

Conference23rd IEEE International Conference on Automation and Computing, ICAC 2017
Country/TerritoryUnited Kingdom
CityHuddersfield
Period7/09/178/09/17

Keywords

  • Anomaly detection
  • Crowd analysis
  • Optical flow
  • SIOF
  • Support Vector Machine

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