Study On Fall Detection And 3D Human Reconstruction Technology Based On Depth Images

Recently,human behavior and posture recognition have become a hot research topic in computer vision due to their broad appeal and significant market potential.Given the severe issue of aging populations,fall detection system and 3D human reconstruction algorithm related to the health of the elderly have become critical areas of focus.However,existing solutions still have shortcomings in terms of efficiency,accuracy,and user privacy protection.Therefore,this paper will use ToF depth images as the foundation and investigate key technologies for human behavior and posture recognition from two perspectives: fall detection and 3D human reconstruction.The objective is to propose effective solutions to address the aforementioned issues.Specifically,the research content and main contributions of this article are as follows:(1)Propose a real-time multi-person fall detection system based on ToF depth images.The algorithm prioritizes real-time performance and accuracy,replacing the computationally expensive recurrent neural network with an improved single-stage object detection network to improve parallelism.By introducing temporal information for judgment,the algorithm effectively reduces false alarms and improves decision accuracy.Specifically,based on the definition of falls,the algorithm divides all possible target postures into three categories and takes the maximum vertical velocity during the off balance status as the fall detection criterion.Extensive experiments on multiple datasets demonstrate the algorithm’s effectiveness,with a detection frame rate of up to 40 FPS in real-time testing,highlighting its high efficiency and real-time performance.(2)Propose a method for 3D human reconstruction based on a single ToF depth image.It represents a first attempt in the field of 3D human reconstruction using only depth images as input.To tackle the problem of dataset scarcity,the model’s training constraints primarily rely on 2D information,which can be obtained through existed algorithms using depth images as input.Building upon 2D keypoint constraints,the algorithm introduces silhouette constraints and a network module based on graph convolution layers to more effectively extract feature information from the depth image,leveraging the topological information provided by the parameterized model.A series of ablation experiments effectively demonstrate the effectiveness of each module in the algorithm,and the algorithm’s excellent performance is fully reflected through both quantitative and qualitative evaluations.