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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Background: Autism Spectrum Disorder (ASD) is a complex developmental condition that involves persistent challenges in social interaction, speech, and nonverbal communication, in addition to repetitive or restrictive behaviours. For decades, children with ASD have been familiarising themselves with information and communication technologies (ICT) in their training and diagnosis. One of the ICT areas, namely non-immersive virtual reality (NIVR), has become a noticeable tool to help ASD children in their social training. It provides extensive virtual interaction, a safe environment, and is affordable. An NIVR application is developed to assist the intervention on ASD children. However, the whole experiences of the training need to be validated to conclude its effectiveness. Methods: A case study was employed as the research method. An evaluation of NIVR application using multiple sources of evidence was guided by Kirkpatrick Model of Evaluation (KME) which was executed via questionnaires, pre- and post-test. The main objectives of this research were to evaluate level 1 and 2 of KME. The target for Level 1 is to assess the reactions to the NIVR application. Level 2 is to gauge the knowledge, confidence, or mindset of participants. Level 2 covers the evaluation prior to the training (pre-test) and after the training (post-test). Results: On average the ASD children had good experiences and were able to improve their social skills with the NIVR application. Therefore, the combination of serious game, analytics and specific VR type provides good data assessment, facilitate comfortable training, and can be an effective intervention for children with ASD. Conclusion: The positive trend on both levels shows that the application has a good potential to be used in ASD training. The results could be improved in a higher number of participants. Currently, only a limited number of research participants could be obtained due to the COVID-19 pandemic.