Clustering of Local Behaviour in Crowd Videos

Date

2014-04-25

Author

Öngün, Cihan
Temizel, Alptekin
Taşkaya Temizel, Tuğba

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

179
views

0
downloads

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 resolution, holistic approaches have started to be preferred rather than detection and tracking of individuals. In this work, a method based on detection of regional behaviors in high density crowds is proposed. The method clusters the crowd behavior in different areas of the scene and can be used as a basis for anomaly detection.

Subject Keywords

Crowd Behavior Analysis, Video Surveillance Applications, Computer Vision

URI

https://hdl.handle.net/11511/55665

Conference Name

22nd IEEE Signal Processing and Communications Applications Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

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)...
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...
Pedestrian zone anomaly detection by non-parametric temporal modelling Gündüz, Ayşe Elvan; Taşkaya Temizel, Tuğba; Temizel, Alptekin (2014-08-29) With the increasing focus on safety and security in public areas, anomaly detection in video surveillance systems has become increasingly more important. In this paper, we describe a method that models the temporal behavior and detects behavioral anomalies in the scene using probabilistic graphical models. The Coupled Hidden Markov Model (CHMM) method that we use shows that sparse features obtained via feature detection and description algorithms are suitable for modeling the temporal behavior patterns and ...
Object tracking system with seamless object handover between stationary and moving camera modes Emeksiz, Deniz; Temizel, Alptekin; Department of Information Systems (2012) As the number of surveillance cameras and mobile platforms with cameras increases, automated detection and tracking of objects on these systems gain importance. There are various tracking methods designed for stationary or moving cameras. For stationary cameras, correspondence based tracking methods along with background subtraction have various advantages such as enabling detection of object entry and exit in a scene. They also provide robust tracking when the camera is static. However, they fail when the ...
Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding Solmaz, Berkan; Gundogdu, Erhan; Yucesoy, Veysel; Koc, Aykut; Alatan, Abdullah Aydın (2018-12-01) Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for ma...

Citation Formats

C. Öngün, A. Temizel, and T. Taşkaya Temizel, “Clustering of Local Behaviour in Crowd Videos,” presented at the 22nd IEEE Signal Processing and Communications Applications Conference (SIU), Karadeniz Teknik Univ, Trabzon, TURKEY, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55665.