Department of Computer Science, HITEC University, Taxila, Pakistan.
Department of Computer Science, University of Wah, Wah Cantt, Pakistan.
Comput Intell Neurosci. 2022 Jul 14;2022:8238375. doi: 10.1155/2022/8238375. eCollection 2022.
Human gait recognition has emerged as a branch of biometric identification in the last decade, focusing on individuals based on several characteristics such as movement, time, and clothing. It is also great for video surveillance applications. The main issue with these techniques is the loss of accuracy and time caused by traditional feature extraction and classification. With advances in deep learning for a variety of applications, particularly video surveillance and biometrics, we proposed a lightweight deep learning method for human gait recognition in this work. The proposed method includes sequential steps-pretrained deep models selection of features classification. Two lightweight pretrained models are initially considered and fine-tuned in terms of additional layers and freezing some middle layers. Following that, models were trained using deep transfer learning, and features were engineered on fully connected and average pooling layers. The fusion is performed using discriminant correlation analysis, which is then optimized using an improved moth-flame optimization algorithm. For final classification, the final optimum features are classified using an extreme learning machine (ELM). The experiments were carried out on two publicly available datasets, CASIA B and TUM GAID, and yielded an average accuracy of 91.20 and 98.60%, respectively. When compared to recent state-of-the-art techniques, the proposed method is found to be more accurate.
在过去的十年中，人类步态识别作为生物识别的一个分支出现，专注于基于运动、时间和服装等多种特征的个体。它也非常适用于视频监控应用。这些技术的主要问题是传统特征提取和分类导致的准确性和时间损失。随着深度学习在各种应用中的进步，特别是在视频监控和生物识别方面，我们在这项工作中提出了一种用于人体步态识别的轻量级深度学习方法。该方法包括顺序步骤-预训练的深度模型特征选择分类。最初考虑了两个轻量级预训练模型，并根据附加层和冻结一些中间层进行了微调。之后，使用深度迁移学习对模型进行训练，并在全连接和平均池化层上设计特征。融合使用判别相关分析进行，然后使用改进的 moth-flame 优化算法对其进行优化。最后，使用极限学习机（ELM）对最终最优特征进行分类。实验在两个公开可用的数据集 CASIA B 和 TUM GAID 上进行，分别得到平均准确率 91.20%和 98.60%。与最近的最先进技术相比，发现该方法更准确。
