arXiv · · Significant research
Researchers developed a validated drone-based crowd counting system designed for large-scale events like the FIFA World Cup 2034 in Saudi Arabia and the Hajj. The system addresses challenges of maintaining accuracy on unlabelled footage and detecting dangerous crowd inflow before a crush forms. It employs label-free adaptation, recovering 31-49% of shift-induced error, establishes a "severity law," and includes a six-point deployment protocol. Why it matters: This research offers a critical advancement in AI safety and crowd management technology, directly supporting Saudi Arabia's capacity to host major global events and ensuring public safety through advanced computer vision techniques.
Drone-based counting · Crowd monitoring · Label-free adaptation · FIFA World Cup 2034 · Saudi Arabia
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arXiv ·
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
arXiv ·
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
arXiv ·
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.
arXiv ·
This paper introduces a deep learning framework for automated pain-level detection, designed for deployment in the UAE healthcare system. The system aims to assist in patient-centric pain management and diagnosis support, particularly relevant in situations with medical staff shortages. The research assesses the framework's performance using common approaches, indicating its potential for accurate pain level identification.