Document Details

Document Type : Thesis 
Document Title :
REAL-TIME FREQUENCY-BASED DETECTION OF A PANIC BEHAVIOR IN HUMAN CROWDS
كشف ذعر الحشود البشرية في الوقت الحقيقي باعتماد نطاق الترددات
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The real-time detection of a panic behavior in a human crowd is of a high interest as it helps alleviating crowd disasters and ensures that timely appropriate action will be taken. However, the fast analysis of video sequences to detect abnormal behaviors is one of the most challenging tasks in computer vision. While many research works propose off-line solutions, few studies investigate the real-time analysis of crowded scenes. This may be due to the fact that detecting a panic behavior is closely related to the analysis of the crowd dynamics, which commonly necessitates heavy computations. In order to alleviate this problem, we propose a real-time panic detection technique that analyzes the crowd movements based on a simple and efficient solution. The key idea of the proposed approach consists of analyzing the interactions between moving edges along the video in the frequency domain. Our contribution is threefold. First, moving edges are considered for analysis along the video. Second, when a panic situation occurs within a human crowd, it leads to interactions between people that are different from those that occur during a normal situation. Therefore, to reveal such a behavior, a new frequency based-feature is proposed. To select the most appropriate frequency domain, the Fast Fourier Transform (FFT), the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT) are investigated. Third, two different formulations of the problem of detecting a panic are explored. The experimental evaluation of the proposed technique shows its outperforming compared to the state-of-the-art approaches in terms of detection rates and execution time. 
Supervisor : Dr. Heyfa Esmaeel Ammar 
Thesis Type : Master Thesis 
Publishing Year : 1440 AH
2019 AD 
Added Date : Monday, July 8, 2019 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
بهيه عماد الديسيAldissi, Bahiya EmadResearcherMaster 

Files

File NameTypeDescription
 44677.pdf pdf 

Back To Researches Page