Depth-Based Real-Time Gait Recognition

Abstract

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

“…Image-based methods extract individual’s silhouettes from a video sequence through background subtraction, and then align and compress them into a single image that represents the final gait representation [ 1 , 2 ]. Gait features are then extracted from the images either using Principal component analysis (PCA) [ 1 ], Linear discriminant analysis (LDA) [ 2 , 23 , 24 ], or CNN [ 25 , 26 , 27 ]. Finally, the similarity between features is computed, for example, by using the cosine similarity.…”

Feature extraction for person gait recognition applications

“…Ramakić et al [7] and Lenac et al [8] presented approaches where they used appearancebased methods, such as GEI and BGEI, and height feature obtained from depth images for gait recognition tasks.…”