Improved Gait Recognition Accuracy Based on DFT- GEI

Person identification is a challenging task in computer vision. Identify a person from different cameras due to changes in appearance based on cofactors. Cofactors such as changing clothes, a suitcase, backpack, etc. The gait biometric is used to identify a person vary with different cofactors at different backgrounds. The person's gait can be identified at a distance, based on a walking pattern, without any physical contact. In this work, the videos are recorded using Infrared and Visible cameras at different locations such as urban and rural environments. The pre-processing includes the recorded videos are converted into frames, person identification using deep learning techniques, background subtraction, artifacts removal, silhouettes extraction, calculating gait cycle, and synthesis frequency domain gait energy image by averaging the silhouettes. The moving features are extracted from the frequency domain gait energy image and gait energy image are dimensionally reduced by principal component analysis, recognized using different classifiers and results are compared. Experiments are conducted on urban and rural datasets recorded using Long Wave Infrared and Visible cameras.

Computer Vision and Abnormal Patient Gait: A Comparison of Methods

Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential-prediction-framework-based approaches, convolutional pose machines, gait energy image, 2-Directional 2-dimensional principles component analysis ((2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI), SVM, ANN, K-Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.