Real-Time Multistep Time-Series Prediction of Driver’s Head Pose During IVIS Secondary Tasks for Human–Machine Codriving and Distraction Warning Systems

Zhao, Xia; Li, Zhao; Zhao, Chen; Wang, Chang

doi:10.1109/jsen.2022.3216057

Cited by 5 publications

(1 citation statement)

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“…Figure shows the spatial coordinates x , y , and z of 33 nodes of the human body captured through MediaPipe pose. [ 55 ] As a machine‐learning scheme for tracking body poses, MediaPipe Pose inferred 33 3D landmarks and background segmentation masks on the whole body from RGB video frames. After data processing, the coordinates x , y , and z were used as the input of the R, G, and B channels in the RGB image to visualize the activity information.…”

Section: Methodsmentioning

confidence: 99%

Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Zhao,

Sun,

et al. 2023

Advanced Intelligent Systems

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Human activity recognition (HAR) has gained popularity in the field of computer vision such as video surveillance, security, and virtual reality. However, traditional methods are limited in terms of computations and holistic learning of human skeletal sequences. In this article, a new time‐series skeleton joint data imaging method is infused into an improved convolutional neural network to handle these problems. First, the raw time‐series data of 33 body nodes are transformed to red–green–blue images by encoding the 3D positional information to one pixel. Second, the LeNet‐5 network is enhanced by expanding the receptive field, introducing coordinate attention and the smooth maximum unit to improve smoothness and feature extraction. Third, the ability of coded images to express human activities is studied in various environments. It is shown in the experimental results that the method achieves an impressive accuracy of 98.02% in recognizing 25 daily human activities, such as running, writing, and walking. In addition, it is shown that the number of floating point operations, parameters, and inference time of the method are 0.08%, 0.47%, and 3.05%, respectively, of the average values for six other networks (including AlexNet, GoogLeNet, and MobileNet). The proposed method is thus a novel, lightweight, and high‐precision solution for HAR.

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Section: Methodsmentioning

confidence: 99%