Automated crowd behavior analysis for video surveillance applications

Güler, Püren
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 behaviors of individuals in a crowd (object based) and using this knowledge to make deductions regarding the crowd behavior and analyzing the crowd as a whole (holistic based). In this work, a holistic approach is used to develop a real-time abnormality detection in crowds using scale invariant feature transform (SIFT) based features and unsupervised machine learning techniques.


Local Anomaly Detection in Crowded Scenes Using Finite-Time Lyapunov Exponent Based Clustering
Öngün, Cihan; Temizel, Alptekin; Taşkaya Temizel, Tuğba (2014-08-29)
Surveillance of crowded public spaces and detection of anomalies from the video is important for public safety and security. While anomaly detection is possible by detection and tracking of individuals in low-density areas, such methods are not reliable in high-density crowded scenes. In this work we propose a holistic unsupervised approach to cluster different behaviors in high density crowds and detect the local anomalies using these clusters. Finite-Time Lyapunov Exponents (FTLE) is used for analyzing th...
Mean-Shift Tracking for Surveillance Applications Using Thermal and Visible Band Data Fusion
Beyan, Cigdem; Temizel, Alptekin (2011-04-28)
Separate tracking of objects such as people and the luggages they carry is important for video surveillance applications as it would allow making higher level inferences and timely detection of potential threats. However, this is a challenging problem and in the literature, people and objects they carry are tracked as a single object. In this study, we propose using thermal imagery in addition to the visible band imagery for tracking in indoor applications (such as airports, metro or railway stations). We u...
Visual privacy protection using false colors
Çiftçi, Serdar; Akyüz, Ahmet Oğuz; Department of Computer Engineering (2017)
Visual privacy protection (VPP) in video surveillance is an important problem, which is likely to be even more important with the rapid expansion of video surveillance. Although many VPP algorithms exist, none of them simultaneously meets all the desired characteristics of a good privacy protection algorithm. The chief limitation of existing VPP methods is that they require regions of interest (ROI) to be automatically detected or manually marked, both of which are difficult to achieve and prone to errors. I...
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 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 ...
Citation Formats
P. Güler, “Automated crowd behavior analysis for video surveillance applications,” M.S. - Master of Science, Middle East Technical University, 2012.