Person re-identification (Re-ID) is an instance-level task of image retrieval, and its identification accuracy depends on the distinguishable features extracted from people. However, most identification methods based on deep learning only mechanically extract distinguishable features of person images, and some important details are frequently overlooked. For scenes with substantial background differences or occlusions, the Re-ID efficiency is not high and the network scalability is not good. Here, the authors propose a multi-scale feature combination network (MFC-Net) model that combines structural feature information with global comprehensive feature information of the person images through a convolution neural network that can effectively retain distinguishing character detail information. The authors also propose a Gaussian stochastic pooling layer to solve the defects of the pooling layer. For the problem of many network parameters and weak performance, the authors propose an attentive feature convolutions layer. The authors perform many comparative experiments on three benchmark datasets. The results prove that our MFC-Net model performs well in person Re-ID and that its identification accuracy is higher than that of other investigated models.
Aimed at the high cost of domestic rehabilitation medical care, the limited number of doctors, the shortage of training venues, and the lack of follow-up tracking for patients who recovered better after rehabilitation training, and a remote monitoring system to understand the patient’s rehabilitation situation, a kind of motion recognition-based Remote monitoring system for physical rehabilitation training based on motion recognition was proposed. From the perspective of machine learning and intelligent classification, the system uses the wavelet transform principle and Support Vector Machine (SVM) algorithm to inject intelligence into the remote monitoring system for limb rehabilitation training, so that doctors can receive patients walking and running energy characteristic and their movement distance data in the rehabilitation center, and based on this data to determine the patient’s recovery and rehabilitation training plan, the doctor can make a diagnosis for dozens or even hundreds of patients even if they never leave home, which greatly improves the efficiency of treatment, saves the corresponding manpower and material resources for the country and society t, and benefits the people.
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