Anomaly detection for video surveillance in crowded environments /

Download

index.pdf

Date

2014

Author

Öngün, Cihan

Metadata

Show full item record

Item Usage Stats

277
views

108
downloads

Crowd behavior analysis and anomaly detection in crowded environments have become more important in recent years. In the literature there are two main approaches for crowd behavior analysis based on the density of the crowd. While individual analysis is more efficient for low and medium density crowds, holistic approaches which consider the crowd as a whole are more efficient for high density crowds. Crowd behavior analysis studies can be examined in 3 categories: group behavior analysis, crowd behavior analysis and anomaly detection. While group behavior analysis is based on detection and tracking of human groups, crowd behavior analysis studies considered the whole crowd in the video. These steps are generally followed by anomaly detection which is the task of detecting the events which are normally not expected in a scene. In this work, the aim is to detect behavioral anomalies in high density crowds where detection and tracking of individuals are difficult. Video scene is considered as a whole and a heat map is generated using Finite-Time Lyapunov Exponents (FTLE) based on motion changes and this heat map is divided into behavioral clusters using hierarchical clustering. Then considering the distribution of these clusters existence of anomaly is determined and abnormal cluster are detected using an adaptive threshold.

Subject Keywords

Computer vision., Video surveillance., Video recording., Collective behavior.

URI

http://etd.lib.metu.edu.tr/upload/12617634/index.pdf
https://hdl.handle.net/11511/23774

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Anomaly detection using sparse features and spatio-temporal hidden markov model for pedestrian zone video surveillance Gündüz, Ayşe Elvan; Taşkaya Temizel, Tuğba; Temizel, Alptekin; Department of Information Systems (2014) Automated analysis of crowd behavior for anomaly detection has become an important issue to ensure the safety and security of the public spaces. Public spaces have varying people density and as such, algorithms are required to work robustly in low to high density crowds. Mainly, there are two different approaches for analyzing the crowd behavior: methods based on object tracking where individuals in a crowd are tracked and holistic methods where the crowd is analyzed as a whole. In this work, the aim is to ...
An unsupervised method for anomaly detection from crowd videos Guler, Puren; Temizel, Alptekin; Temizel, Tugba Taskaya (2013-01-01) Anomaly detection from crowd videos is an issue that is becoming more important due to the difficulties in maintaining the public security in crowded places. Surveillance videos has a significant role for enabling the real time analysis of the captured events occurring in crowded places. This paper presents a method that detects anomalies in crowd in real-time using computer vision and machine learning techniques. The proposed method consists of extracting the crowd behavior properties (velocity, direction)...
Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2016-10-16) In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories via feature covariance matrices enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances between trajectories, anomaly detection is achieved by sparse repres...
Automated crowd behavior analysis for video surveillance applications Güler, Püren; Temizel, Alptekin; Taşkaya Temizel, Tuğba; Department of Information Systems (2012) Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups; people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behavi...
Feature Detection and Tracking for Extraction of Crowd Dynamics Gunduz, Ayse Elvan; Temizel, Alptekin; Temizel, Tugba Taskaya (2013-01-01) Extraction of crowd dynamics from video is the fundamental step for automatic detection of abnormal events. However, it is difficult to obtain sufficient performance with object tracking due to occlusions and insufficient resolution of the objects in the scene. As a result, optical flow or feature tracking methods are preferred in crowd videos. These applications also require algorithms to work in real-time. In this work, we investigated the applicability and performance of feature detection and tracking al...

Citation Formats

C. Öngün, “Anomaly detection for video surveillance in crowded environments /,” M.S. - Master of Science, Middle East Technical University, 2014.