Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model’s ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach.

Annually, a huge number of pilgrims visit Mecca to perform Al Hajj ritual. Crowd management is critical in this occasion in order to avoid crowd disasters (e.g., stampede and suffocation). Recent studies stated that various factors, such as the environment, fatigue level, health condition and emotional status have a significant effect on crowded events. This calls for a need for an automated data analytics system that feeds event organizers with information about those factors on real-time, at least from a generalizable sample of crowd subjects, in which proactive crowd management decisions are made to reduce overall risks. This paper develops a novel methodology that fuses mobile GPS and physiological data of Hajj pilgrims collected through wearable sensors to train three classification models: (a) current performed Hajj activity, (b) fatigue level, and (c) emotional level. In a pilot experiment conducted against two subjects, promising results of a minimum of 75% accuracy levels were achieved for the activity recognition and fatigue level classifiers, whereas the emotional level classifier still requires further refinements.

Automated crowd counting is a crucial aspect of surveillance, especially in the context of mass events attended by large populations. Traditional methods of manually counting the people attending an event are error-prone, necessitating the development of automated methods. Accurately estimating crowd counts across diverse scenes is challenging due to high variations in the sizes of human heads. Regression-based crowd-counting methods often overestimate counts in low-density situations, while detection-based models struggle in high-density scenarios to precisely detect the head. In this work, we propose a unified framework that integrates regression and detection models to estimate the crowd count in diverse scenes. Our approach leverages a routing strategy based on crowd density variations within an image. By classifying image patches into density levels and employing a Patch-Routing Module (PRM) for routing, the framework directs patches to either the Detection or Regression Network to estimate the crowd count. The proposed framework demonstrates superior performance across various datasets, showcasing its effectiveness in handling diverse scenes. By effectively integrating regression and detection models, our approach offers a comprehensive solution for accurate crowd counting in scenarios ranging from low-density to high-density situations.

Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.

This study aims to create and examine a multimodal dataset to enhance crowd management during the Hajj seasons. Sixty-four participants were engaged in Hajj rituals such as Tawaf, Saai, prayer, and Doaa providing location and peripheral physiological data, collected and annotated using a custom-made smartphone application. The collected data was leveraged to conduct a comprehensive analysis, specifically focusing on the classification of the type of Hajj activity, level of fatigue, and emotional states based on peripheral physiological signals. Three deep learning classification models were developed and validated using feedforward neural networks. The models achieved satisfactory accuracy scores in classifying the type of Hajj activity (41.71%), level of fatigue (85.27%), and emotional states (82.47%). While presenting a straightforward use case, this research chiefly provides decision makers and the scientific community with a statistically significant open data set aside with a deep learning architecture capable of characterizing crowd behavior for the purpose of automating crowd management and monitoring.

With the rapid growth of urbanization, large public gatherings in the form of religious festivals, marathons, political rallies are commonly observed in modern cities. Ensuring public safety and security is one the main responsibilities of law enforcement agencies during such events. Despite safety measures, crowd disaster often occurs during such events which leads to the fatalities and injuries. Therefore, effective crowd management is important to ensure public safety and support resilient urban life in smart cities. Traditional crowd management systems rely on manual monitoring and analysis of video streams by human analyst. These methods are slow and labor-intensive and are not suitable for real-time decision making. To address this challenge, we propose a framework based on advanced deep learning approach that detects and localizes congested areas in crowded scenes. Our framework utilizes recurrence plots (RP) images to encode irregularities in crowd motion. These RP images are then classified using a specialized convolutional neural network tailored for congestion detection. We use both synthetic and real-world datasets to evaluate the performance of proposed framework. From the extensive experimental results, we conclude that proposed framework achieves better performance in detecting congested areas compared to state-of-the-art methods.