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

Berkan Solmaz

doi:10.1117/12.2325527

8 October 2018 Video-based detection of abnormal activities in crowd using a combination of motion-based features

Berkan Solmaz

Proceedings Volume 10802, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II; 108020K (2018) https://doi.org/10.1117/12.2325527
Event: SPIE Security + Defence, 2018, Berlin, Germany

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.

Conference Presentation

Citation Download Citation

Berkan Solmaz "Video-based detection of abnormal activities in crowd using a combination of motion-based features", Proc. SPIE 10802, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II, 108020K (8 October 2018); https://doi.org/10.1117/12.2325527

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available