An unsupervised method for anomaly detection from crowd videos

Guler, Puren
Temizel, Alptekin
Temizel, Tugba Taskaya
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) by tracking scale invariant feature transform (SIFT) feature points and fitting the extracted behavior properties into a Gaussian Model. In this paper, only the global anomalies which occur on the overall video frame are handled. According to the test results, the method gives comparable results with the state-of-art methods and also can run in real-time. In addition, it is less complex than the compared state-of-art methods and works unsupervised.


Clustering of Local Behaviour in Crowd Videos
Öngün, Cihan; Temizel, Alptekin; Taşkaya Temizel, Tuğba (2014-04-25)
Surveillance cameras are playing more important role in our daily life with the increasing number of human population and surveillance cameras. While there are a myriad of methods for video analysis, they are generally designed for low-density areas. Running of these algorithms in crowded areas would not give expected results and results in high number of false alarms giving rise to a need for different approaches for crowded area surveillance. Due to occlusions and images of individuals having a low resolu...
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 ...
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...
Fusion of thermal- and visible-band video for abandoned object detection
Beyan, Cigdem; Yiğit, Ahmet; Temizel, Alptekin (2011-07-01)
Timely detection of packages that are left unattended in public spaces is a security concern, and rapid detection is important for prevention of potential threats. Because constant surveillance of such places is challenging and labor intensive, automated abandoned-object-detection systems aiding operators have started to be widely used. In many studies, stationary objects, such as people sitting on a bench, are also detected as suspicious objects due to abandoned items being defined as items newly added to ...
Anomaly detection for video surveillance in crowded environments /
Öngün, Cihan; Ulusoy, İlkay; Temizel, Alptekin; Department of Electrical and Electronics Engineering (2014)
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 ana...
Citation Formats
P. Guler, A. Temizel, and T. T. Temizel, “An unsupervised method for anomaly detection from crowd videos,” 2013, Accessed: 00, 2020. [Online]. Available: