Multi-gait recognition using hypergraph partition

Chen, Xin; Xu, Jiaming; Weng, Jian

doi:10.1007/s00138-016-0810-6

Cited by 18 publications

(4 citation statements)

References 44 publications

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“…As the number of users of application data increases, integrated management of user information and data permissions is required [37]. Therefore, the data service platform must provide user management services in data applications, create databases and user databases in the applications, and manage user access to basic data and data.…”

Section: Design and Implementation Of Datamentioning

confidence: 99%

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

2022

Wireless Communications and Mobile Computing

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In this article, we provide a detection method based on a convolutional neural network algorithm. Preamble detection is very important in underwater acoustic communication. Only the preamble signal can trigger the receiving operation to effectively process the acoustic data received underwater. The prerequisite for specifying the joint position of the distributed acoustic multiarray is the mutual calibration calculation of each array. This paper designs and develops a prototype system for microphone array sound source positioning and provides an optimized system to obtain calibration source mapping points for distributed sound transmission mutual calibration, and for mutual distributed acoustic microphone arrays, use epipolar geometry and normalization algorithm for calibration to provide a basis for a mutual calibration calculation model and functional module design. Finally, perform numerical simulation verification. It provides a design plan and implementation method for the Internet-based smart city education system, aiming to achieve professional education and educational needs related to the Internet of Things. First, we will take Internet application technology as an example to analyze the functional requirements of the education system, including the positioning of human resource development, integrated sensors, C/C#/Java application development technology, and other technical means to realize the smart city education system. Based on the theory of business management, this paper studies the business planning issues of smart city construction. The main content is divided into three parts: business composition and management and planning management. This is an important part of business. It plays a decisive role in the overall planning and implementation stage and protects the implementation progress of the project and ensures the quality of the project by including important content.

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Section: Design and Implementation Of Datamentioning

confidence: 99%

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

2022

Wireless Communications and Mobile Computing

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“…The use of vision sensors led to the beginning of gait recognition analyses [21]; follow-up studies have actively been carried out in the literature [22,23]. Despite challenging conditions for collecting data in vision-based recognition (e.g., requiring only the subject of interest in the video sequences), the accuracy of gait recognition based on these vision-based approaches is insufficiently high and yet unstable depending on the viewpoint and orientation of the sensing devices [24]. To overcome these obstacles, not only subjects in video sequences were segmented and individually tracked [24], but also 3D construction and view transformation models were used [25].…”

Section: Gait Recognitionmentioning

confidence: 99%

“…Despite challenging conditions for collecting data in vision-based recognition (e.g., requiring only the subject of interest in the video sequences), the accuracy of gait recognition based on these vision-based approaches is insufficiently high and yet unstable depending on the viewpoint and orientation of the sensing devices [24]. To overcome these obstacles, not only subjects in video sequences were segmented and individually tracked [24], but also 3D construction and view transformation models were used [25]. A view-adaptive mapping approach for gait recognition was also developed in [26] to alleviate the free-view gait recognition problem in which the view angle is often unknown, dynamically changing, or does not belong to any predefined views.…”

Section: Gait Recognitionmentioning

confidence: 99%

Explainable gait recognition with prototyping encoder–decoder

et al. 2022

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Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder–decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).

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“…Moreover, the sensing devices' viewpoint and the orientation also affect the accuracy. To recognize a subject from a video sequence which includes more than one person, each subject should be segmented and tracked individually [30,31]. To achieve a stable recognition accuracy regardless sensing devices' viewpoint and orientation, 3D construction model or view transformation model can be utilized [32][33][34].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Few-Shot Learning for Gait Recognition

Moon

Minaya

et al. 2020

Applied Sciences

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A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

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Multi-gait recognition using hypergraph partition

Cited by 18 publications

References 44 publications

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

Distributed Sound Transmission and Smart City Planning Management Based on Convolutional Neural Network

Explainable gait recognition with prototyping encoder–decoder

Multimodal Few-Shot Learning for Gait Recognition

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