Multi-level threat analysis in anomalous crowd videos

Arindam Sikdar*, Ananda S. Chowdhury

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

1 Citation (Scopus)

Abstract

Crowd anomaly detection is a challenging problem in the field of computer vision. An abnormal event in a crowd scene can be labeled as threat in a video. Several existing solutions in this area have marked video frames either normal or abnormal event. Such categorization of frames can be referred as two-class threat labeling problem. However, this notion of two-class threat labeling is not well defined in literature. An event can have multiple aspects as it can be treated as anomalous or non-anomalous based on the situation of occurrence. Based on this argument, we propose a new paradigm of extending this two class threat labeling problem to multi-class labeling. As a solution to this multi-class labeling problem, we cluster frames with low, medium and high threat. We also propose a new feature known as pseudo-entropy for better clustering of threats. Our framework consists of two main components, namely, Earth mover distance (EMD) based anomaly detection system and multi-level threat analysis. As an outcome frame-wise and segment-wise threat representation are also presented to facilitate real time video search for relevant events. Exhaustive internal comparison and statistical analysis over benchmark UCSD and UMN dataset clearly indicates the merit of the proposed framework.

Original languageEnglish
Title of host publicationComputer Vision and Image Processing - 4th International Conference, CVIP 2019, Revised Selected Papers
EditorsNeeta Nain, Santosh Kumar Vipparthi, Balasubramanian Raman
PublisherSpringer
Pages495-506
Number of pages12
ISBN (Print)9789811540172
DOIs
Publication statusPublished - 29 Mar 2020
Event4th International Conference on Computer Vision and Image Processing, CVIP 2019 - Jaipur, India
Duration: 27 Sept 201929 Sept 2019

Publication series

NameCommunications in Computer and Information Science
Volume1148 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference4th International Conference on Computer Vision and Image Processing, CVIP 2019
Country/TerritoryIndia
CityJaipur
Period27/09/1929/09/19

Keywords

  • Crowd anomaly
  • Frame-wise pattern
  • Local motion descriptor
  • Multi-level threat
  • Segment-wise pattern

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