Old Man Fall Detection Based on Surveillance Video Object Tracking Image recognition technology based on deep learning has made great progress, which makes object detection technology work in many fields. The number of elderly people in China has risen year by year, proclaiming the arrival of an aging society. xe2x80x9cThe old man canxe2x80x99t fallxe2x80x9d is a consensus. Using object detection algorithm to detect the fall of the elderly is a research hotspot in the field of object detection. Through the analysis of the object detection algorithm and the object tracking algorithm, Deepsort and YOLOv3 algorithms are used to achieve the realtime fall detection of the surveillance video. The experimental results prove that combined with YOLOv3 and the Deepsort algorithms can detect the fall of the elderly.

Elderly Health Monitoring System with Fall Detection Using MultiFeature Based Person Tracking The need for personalized surveillance systems for elderly health care has risen drastically. However, recent methods involving the usage of wearable devices for activity monitoring offer limited solutions. To address this issue, we have proposed a system that incorporates a visionbased deep learning solution for elderly surveillance. This system primarily consists of a novel multifeaturebased person tracker (MFPT), supported by an efficient visionbased person fall detector (VPFD). The MFPT encompasses a combination of appearance and motion similarity in order to perform effective target association for object tracking. The similarity computations are carried out through Siamese convolutional neural networks (CNNs) and longshort term memory (LSTM). The VPFD employs histogramoforientedgradients (HoGs) for feature extraction, followed by the LSTM network for fall classification. The cloudbased storage and retrieval of objects is employed allowing the two models to work in a distributed manner. The proposed system meets the objectives of ITU Focus Group on AI for Health (FGAI4H)under the category, xe2x80x9cfalls among the elderlyxe2x80x9d. The system also complies with ITUT F.743.1 standard, and it has been evaluated over benchmarked object tracking and fall detection datasets. The evaluation results show that our system achieves the tracking precision of 94.67% and the accuracy of 98.01% in fall detection, making it practical for health care system use. The HoG featurebased LSTM model is a promising item to be standardized in ITU for fall detection in elderly healthcare management under the requirements and service description provided by ITUT F.743.1.