2021/06/06 by Ravi Shekhar Tiwari, Supraja P, P. Supraja +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gait Recognition and Analysis #Hand Gesture Recognition Systems #Human Pose and Action Recognition #This paper tells us how human can be identified by their Gait cycle using any simple camera #Video Surveillance and Tracking Methods #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2106.03136
arxiv created 2021/06/06 · openalex publication_date 2021/06/06 · arxiv updated 2021/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to extract the exact walking features of individuals. Walking features depends on biological as well as the physical feature of the object; hence, it is unique to every individual. In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network on these gait features. The model is trained and evaluated on the dataset acquired by CASIA B Gait, which consists of 15000 videos of 124 subjects walking pattern captured from 11 different angles carrying objects such as bag and coat. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features. The experimental results are compared with amongst accuracies of silhouettes as datasets for training and skeletonized image as training data. The results show that extracting the information from skeletonized data yields improved accuracy.