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 introduces a novel approach for monitoring and analyzing the evolution of complex geographic objects in satellite image time-series. The method uses a spatiotemporal graph and constraint satisfaction problems (CSP) to model and analyze object changes. Experiments on real-world satellite images from Saudi Arabian cities demonstrate the effectiveness of the proposed approach.
MBZUAI ·
MBZUAI researchers are presenting a new approach to open-world object detection at the AAAI conference. The method enables machines to distinguish between known and unknown objects in images, and then learn to classify the unknown objects. PhD student Sahal Shaji Mullappilly is the lead author of the study, titled "Semi-Supervised Open-World Detection". Why it matters: This research addresses a key limitation in current object detection systems, allowing for more adaptable and robust AI in real-world applications.
Inception ·
Researchers at the University of Maryland have developed an AI system that can identify objects hidden by camouflage. The AI uses a convolutional neural network trained on synthetic data to detect partially occluded objects. The system outperformed existing object detection methods in tests on real-world images. Why it matters: The work demonstrates potential applications of AI in defense, security, and search and rescue operations in the Middle East and elsewhere.
arXiv ·
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
KAUST ·
KAUST researchers are using AI to analyze satellite imagery for the automated detection of ancient stone structures in northwest Saudi Arabia, including mustatils (rectangular structures dating to the late 6th millennium BCE) and ruins in circular and triangular shapes. They developed a deep learning algorithm trained on manually identified datasets to isolate similar features over a wide area. The tool converts detected pixels into geodetic coordinates using GPS, assembling them into an online map and database. Why it matters: This project exemplifies computational archaeology, speeding up archaeological discoveries, promoting cultural heritage, and providing a methodology useful to other sectors of the economy.
arXiv ·
This paper surveys machine learning approaches using monument pictures for analyzing heritage sites in India. It addresses challenges in the tourism sector, such as the unavailability of trained personnel and the lack of accurate information. The research aims to provide insights for building an automated decision system to modernize the tourism experience for visitors in India.
arXiv ·
Researchers present RUR53, an unmanned ground vehicle (UGV) capable of autonomous navigation, object recognition, and tool manipulation. The UGV uses a modular software architecture, enabling it to perform complex tasks like detecting panels, docking, and manipulating tools such as wrenches and valve stems. RUR53 was tested at the 2017 Mohamed Bin Zayed International Robotics Challenge where it ranked third in the Grand Challenge as part of a collaboration. Why it matters: This research demonstrates advanced robotics capabilities applicable to various industrial and inspection tasks, highlighting the UAE's focus on robotics innovation.