Human Gait Recognition Using Bag of Words Feature Representation Method

TL;DR

This paper introduces a novel gait recognition method using a bag-of-words feature representation, demonstrating significant accuracy improvements over traditional statistical features across multiple classifiers.

Contribution

The paper presents a new gait recognition approach based on bag-of-words features, evaluated on a unique dataset of 93 individuals, showing improved accuracy over existing statistical methods.

Findings

Significant accuracy improvement over statistical features

Effective on a dataset of 93 individuals

Consistent results across various classifiers

Abstract

In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked with comfortable pace between two end points during two different sessions. To evaluate the effectiveness of the proposed model, the results are compared with the outputs of the classification using extracted features. As it is presented, the proposed method results in significant improvement accuracy compared to using common statistical features, in all the used classifiers.