Incident Detection

Incident detection research focuses on rapidly and accurately identifying events like traffic accidents, natural disasters, or cloud service failures to minimize negative consequences. Current efforts leverage machine learning, employing models such as convolutional neural networks and deep learning architectures, often trained on large datasets of images, videos, sensor data, or crowdsourced reports to improve detection accuracy and reduce false alarms. This work is crucial for optimizing emergency response, improving infrastructure reliability, and enhancing overall safety and efficiency across various sectors. A key trend is the development of data-centric approaches, including weak supervision techniques, to address challenges like noisy or incomplete data.