Human Gait Recognition Based on Frontal-View Walking Sequences Using Multi-modal Feature Representations and Learning

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Processing Letters Pub Date : 2024-04-02 DOI:10.1007/s11063-024-11554-8

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引用次数: 0

Abstract

Despite that much progress has been reported in gait recognition, most of these existing works adopt lateral-view parameters as gait features, which requires large area of data collection environment and limits the applications of gait recognition in real-world practice. In this paper, we adopt frontal-view walking sequences rather than lateral-view sequences and propose a new gait recognition method based on multi-modal feature representations and learning. Specifically, we characterize walking sequences with two different kinds of frontal-view gait features representations, including holistic silhouette and dense optical flow. Pedestrian regions extraction is achieved by an improved YOLOv7 algorithm called Gait-YOLO algorithm to eliminate the effects of background interference. Multi-modal fusion module (MFM) is proposed to explore the intrinsic connections between silhouette and dense optical flow features by using squeeze and excitation operations at the channel and spatial levels. Gait feature encoder is further used to extract global walking characteristics, enabling efficient multi-modal information fusion. To validate the efficacy of the proposed method, we conduct experiments on CASIA-B and OUMVLP gait databases and compare performance of our proposed method with other existing state-of-the-art gait recognition methods.

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利用多模态特征表征和学习，基于正面视图行走序列的人类步态识别

摘要尽管在步态识别方面已经取得了很多进展，但现有的这些工作大多采用侧视参数作为步态特征，这需要大面积的数据采集环境，限制了步态识别在现实世界中的应用。在本文中，我们采用正面视角的行走序列而非侧视序列，并提出了一种基于多模态特征表征和学习的新步态识别方法。具体来说，我们使用两种不同的正面视角步态特征表征来描述行走序列，包括整体轮廓和密集光流。行人区域提取是通过一种名为 Gait-YOLO 算法的改进型 YOLOv7 算法来实现的，以消除背景干扰的影响。提出了多模态融合模块（MFM），通过在通道和空间层面使用挤压和激励操作，探索剪影和密集光流特征之间的内在联系。步态特征编码器进一步用于提取全局行走特征，从而实现高效的多模态信息融合。为了验证所提方法的有效性，我们在 CASIA-B 和 OUMVLP 步态数据库上进行了实验，并将所提方法的性能与其他现有的最先进步态识别方法进行了比较。

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来源期刊

Neural Processing Letters 工程技术-计算机：人工智能

CiteScore

4.90

自引率

12.90%

发文量

392

审稿时长

2.8 months

期刊介绍： Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters