Abnormal Behavior Detection and Warning Based on Deep Intelligent Video Analysis for Geriatric Patients

With the rapid growth of the aging population, abnormal detection has become a key issue in the medical and health field. Accurately detecting abnormal events in the monitoring video and giving feedback in real time can effectively reduce injuries even deaths caused by falls in the elderly. A novel Deepgait-based event detection method is proposed in this study for the elderly monitoring application. The classes of this problem contains abnormal behaviors, unusual events and normal daily life activities. To solve the above recognition problem, a novel deep convolutional features combined with joint Bayesian is proposed to model view variance. To evaluate the performance of the proposed model, two open behavioral data sets: Multiple cameras fall dataset and UR Fall Detection Dataset are adopted, where the mAP value of the improved Deepgait algorithm is 2.23% higher than that of Deepgait algorithm, and 5.4% higher than that of energy model (SSD) approach. The results suggest that our proposed monitoring system is an effective method for the elderly monitoring application.