Розробка системи розпізнавання ходи

Abstract

In this era of innovation and technology, new things are being introduced daily in every field. Recently, a new biometric named gait is being researched a lot to develop it into biometric authentication and recognition system for intelligent surveillance. Gait is defined as a person’s way of walking and is unique to every person. Gait has various attractive properties making it superior to other biometrics. The aim of the project is to design a new gait recognition system based on one of the latest deep learning residuals convolutional neural network ResNet34. The results of applying ResNet34 to publicly available datasets CASIA-B clearly indicates that this model shows promise in this area. To develop a completely functional product using the proposed model, I first collected gait data of an individual by making them walk in front of camera several times, for this I took the online video as I was not able to go to the university and use pedestrian camera. Background subtraction is then applied to the video frames to extract silhouettes and various morphological operations to remove noise and shadows. Gait Energy Image is used as feature extraction technique and finally a pretrained ResNet34 model is used to perform identification at real-time for this I used the CASIA-B samples as collecting my own data was not possible. Comparing the obtained results with other state-of-the-art models, my model achieves highest accuracy in more than half of the cases. This indicates that ResNet34 can achieve ground-breaking results in gait recognition field. In future, using better pretreatment and feature extraction techniques, I strongly believe that ResNet34 can be the next state-of-the-art model in gait recognition.