OptiGait-LGBM: An Efficient Approach of Gait-based Person Re-identification in Non-Overlapping Regions

TL;DR

This paper introduces OptiGait-LGBM, a low-cost, memory-efficient gait recognition model using skeletal landmarks, designed for real-world outdoor environments with non-overlapping cameras and varying conditions.

Contribution

It presents a novel skeletal model-based gait recognition approach and a new benchmark dataset, RUET-GAIT, addressing real-world challenges in gait-based person re-identification.

Findings

Outperforms ensemble methods like Random Forest and CatBoost in accuracy.

Reduces memory usage and training time compared to existing methods.

Effective in uncontrolled outdoor environments with minimal computational cost.

Abstract

Gait recognition, known for its ability to identify individuals from a distance, has gained significant attention in recent times due to its non-intrusive verification. While video-based gait identification systems perform well on large public datasets, their performance drops when applied to real-world, unconstrained gait data due to various factors. Among these, uncontrolled outdoor environments, non-overlapping camera views, varying illumination, and computational efficiency are core challenges in gait-based authentication. Currently, no dataset addresses all these challenges simultaneously. In this paper, we propose an OptiGait-LGBM model capable of recognizing person re-identification under these constraints using a skeletal model approach, which helps mitigate inconsistencies in a person's appearance. The model constructs a dataset from landmark positions, minimizing memory usage by using non-sequential data. A benchmark dataset, RUET-GAIT, is introduced to represent uncontrolled gait sequences in complex outdoor environments. The process involves extracting skeletal joint landmarks, generating numerical datasets, and developing an OptiGait-LGBM gait classification model. Our aim is to address the aforementioned challenges with minimal computational cost compared to existing methods. A comparative analysis with ensemble techniques such as Random Forest and CatBoost demonstrates that the proposed approach outperforms them in terms of accuracy, memory usage, and training time. This method provides a novel, low-cost, and memory-efficient video-based gait recognition solution for real-world scenarios.