A Gait Recognition Method for Human Following in Service Robots

Chi, Wenzheng; Wang, Jiaole; Meng, Max Q.-H.

doi:10.1109/tsmc.2017.2660547

Cited by 63 publications

(26 citation statements)

References 35 publications

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“…H UMAN action recognition is necessary for various computer vision applications that demand information of people's behavior, including surveillance for public safety, human-computer interaction applications, and robotics [1], [2] and [3]. However, action recognition in colored images is challenging task due to several factors, such as complex background, illumination variation, and clothing color, which make it difficult to segment the human body in every scene.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures

Kamel

Sheng

Yang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

177

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In this paper, we present a method for human action recognition from depth images and posture data using convolutional neural networks (CNN). Two input descriptors are used for action representation. The first input is a depth motion image (DMI) that accumulates consecutive depth images of a human action, whilst the second input is a proposed moving joints descriptor (MJD) which represents the motion of body joints over time. In order to maximize feature extraction for accurate action classification, three CNN channels are trained with different inputs. The first channel is trained with depth motion images, the second channel is trained with both depth motion images and moving joint descriptors together, and the third channel is trained with moving joint descriptors only. The action predictions from the three CNN channels are fused together for the final action classification. We propose several fusion score operations to maximize the score of the right action. The experiments show that the results of fusing the output of three channels are better than using one channel or fusing two channels only. Our proposed method was evaluated on three public datasets: MSRAction3D, UTD-MAHD, and MAD dataset. The testing results indicate that the proposed approach outperforms most of existing state of the art methods such as HON4D and Actionlet on MSRAction3D. Although MAD dataset contains a high number of actions (35 actions) compared to existing action RGB-D datasets, our work achieves 91.86% of accuracy.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures

Kamel

Sheng

Yang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

177

View full text Add to dashboard Cite

show abstract

“…Several works [15][16][17][18][19][20] use the first approach to approximate the distance information by triangulation methods applied on two or more RGB views of the same scene. However, the most used visual sensors for person detection are RGB-D cameras [21][22][23][24][25][26][27][28][29][30][31][32] that are able to get both RGB images and depth maps by exploiting infrared light. Several methods employ sensor fusion techniques to merge information from different kinds of sensing systems.…”

Section: Person Followingmentioning

confidence: 99%

“…Focusing on vision-based methods, different strategies can be adopted to detect the person in the environment. Mi et al [26], Ren et al [25] and Chi et al [29] all adopted the Microsoft Kinect SDK that directly provides skeleton position. Satake et al [16,17] used manually designed templates to extract relevant features and find the target location.…”

Section: Person Followingmentioning

confidence: 99%

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

et al. 2020

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The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.

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“…On the other hand, depth sensors technology, such as Kinect, enables realtime 3D human skeleton tracking [10], [11]. Supported by recent advances in robot vision and artificial intelligence, it is possible to monitor a patient's health while carrying out daily activities [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

3-D Canonical Pose Estimation and Abnormal Gait Recognition With a Single RGB-D Camera

Guo

Deligianni

et al. 2019

IEEE Robot. Autom. Lett.

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Assistive robots play an important role in improving the quality of life of patients at home. Among all the monitoring tasks, gait disorders are prevalent in elderly and people with neurological conditions, which increases the risk of fall. Therefore, the development of mobile systems for gait monitoring at home in normal living conditions is important. Here we present a mobile system that is able to track humans and analyze their gait in canonical coordinates based on a single RGB-D camera. Firstly, view-invariant 3D lower limb pose estimation is achieved by fusing information from depth images along with 2D joints derived in RGB images. Next, both the 6D camera pose and the 3D lower limb skeleton are real-time tracked in a canonical coordinate system based on Simultaneously Localization and Mapping (SLAM). A maskbased strategy is exploited to improve the re-localization of the SLAM in dynamic environments. Abnormal gait is detected by using the Support Vector Machine (SVM) and the Bidirectional Long-Short Term Memory (BiLSTM) network with respect to a set of extracted gait features. To evaluate the robustness of the system, we collected multi-camera, ground truth data from sixteen healthy volunteers performing six gait patterns that mimic common gait abnormalities. The experiment results demonstrate that our proposed system can achieve good lower limb pose estimation and superior recognition accuracy compared to previous abnormal gait detection methods.

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A Gait Recognition Method for Human Following in Service Robots

Cited by 63 publications

References 35 publications

Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures

Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

3-D Canonical Pose Estimation and Abnormal Gait Recognition With a Single RGB-D Camera

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