Optical Flow-Based Algorithms for Real-Time Awareness of Hazardous Events

Safety and security are major priorities in modern society. Especially for vulnerable groups of individuals, such as the elderly and patients with disabilities, providing a safe environment and adequate alerting for debilitating events and situations can be critical. Wearable devices can be effective but require frequent maintenance and can be obstructive or stigmatizing. Video monitoring by trained operators solves those issues but requires human resources, time and attention and may present certain privacy issues. We propose optical flow-based automated approaches for a multitude of situation awareness and event alerting challenges. The core of our method is an algorithm providing the reconstruction of global movement parameters from video sequences. This way, the computationally most intensive task is performed once and the output is dispatched to a variety of modules dedicated to detecting adverse events such as convulsive seizures, falls, apnea and signs of possible post-seizure arrests. The software modules can operate separately or in parallel as required. Our results show that the optical flow-based detectors provide robust performance and are suitable for real-time alerting systems. In addition, the optical flow reconstruction is applicable to real-time tracking and stabilizing video sequences. The proposed system is already functional and undergoes field trials for cases of epileptic patients.