Video-based detection of abnormal activities in crowd using a combination of motion-based features

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

Abstract

Visual surveillance is of utmost importance for ensuring public safety and, detecting and preventing violent activities. The rapidly increasing number of surveillance cameras makes automated visual surveillance necessary, since monitoring a large of number of cameras by operators is not feasible, requiring a huge workload. In this paper, we propose a compact method for automated analysis of behaviours in crowds, specifically detecting the abnormal activities in crowd videos, which is one of the most critical applications of visual surveillance. The most intuitive way of abnormal activity detection is to consider common and typical activities in the scenes as normal and any unseen strange activities as anomalies that might be due to dangerous events. When tragic incidents such as accidents, disasters, shootings and violent behaviours happen, people tend to move in a very fast pace and in arbitrary directions. Thus, the proposed method consists of modelling the activities of crowds in the scenes during regular events, and analysing the spatial and temporal changes in their motion, which may be related to abnormal activities. For defining the crowd activities, first, crowd specific motion representations are computed. The computed representations utilize motion attributes such as speed, direction and acceleration of people in the crowds. Next, by employing these representations, typical activities in crowd videos, related to normal behaviours of people when no abnormal activities are present, are learned. Later, the distributions of motion representations are inspected; abrupt changes in the distributions of motion representations, occurring in several parts of the scenes, are labelled as anomalies. Experiments, conducted on a publicly available dataset, involving videos of crowds, reveal that the proposed method is effective in detecting abnormal activities. Additionally, quantitative performance of variants of the proposed method including the baseline approaches were measured for a comparison.

Original languageEnglish (US)
Title of host publicationCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II
EditorsHenri Bouma, Radhakrishna Prabhu, Robert James Stokes, Yitzhak Yitzhaky
PublisherSPIE
ISBN (Electronic)9781510621879
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
EventCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II 2018 - Berlin, Germany
Duration: Sep 10 2018Sep 11 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10802
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceCounterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II 2018
CountryGermany
CityBerlin
Period9/10/189/11/18

    Fingerprint

Keywords

  • Abnormal behaviour detection
  • Anomaly detection
  • Crowd activity.
  • Video analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Solmaz, B. (2018). Video-based detection of abnormal activities in crowd using a combination of motion-based features. In H. Bouma, R. Prabhu, R. J. Stokes, & Y. Yitzhaky (Eds.), Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II [108020K] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10802). SPIE. https://doi.org/10.1117/12.2325527