In crowd surveillance systems, it is important to select the proper analysis algorithm considering the properties of the video content. The inappropriate algorithm selection may result in performance degradation and generation of false alarms. An important feature of crowd videos is the density of the crowd. While object detection and tracking based algorithms are feasible for low density crowds, holistic approaches are preferable for high density crowds. In this paper, we studied the problem of crowd density classification and reported the accuracy rates and execution times in comparison with the studies in the literature.


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)...
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...
SVAS: Surveillance Video Analysis System
Kardas, Karani; Çiçekli, Fehime Nihan (2017-12-15)
This paper introduces a Surveillance Video Analysis System, called SVAS, for surveillance domain, in which the semantic rules and the definition of event models can be learned or defined by the user for automatic detection and inference of complex video events. In the scope of SVAS, an event model method named Interval-Based Spatio-Temporal Model (IBSTM) is proposed. SVAS can learn action models and event models without any predefined threshold values and generates understandable and manageable IBSTM event ...
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...
An image encryption algorithm robust to post-encryption bitrate conversion
Akdağ, Sadık Bahaettin; Candan, Çağatay; Department of Electrical and Electronics Engineering (2006)
In this study, a new method is proposed to protect JPEG still images through encryption by employing integer-to-integer transforms and frequency domain scrambling in DCT channels. Different from existing methods in the literature, the encrypted image can be further compressed, i.e. transcoded, after the encryption. The method provides selective encryption/security level with the adjustment of its parameters. The encryption method is tested with various images and compared with the methods in the literature ...
Citation Formats
A. E. Gunduz, T. Taşkaya Temizel, and A. Temizel, “DENSITY ESTIMATION 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: