Intelligent Recognition of Fatigue and Sleepiness Based on InceptionV3-LSTM via Multi-Feature Fusion

Zhao, Yifei; Xie, Kai; Zou, Zizhuang; He, Jing

doi:10.1109/access.2020.3014508

Cited by 34 publications

(17 citation statements)

References 44 publications

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“…In the formula, conv3(•) is the convolution operation of the feature graph and + is the polar addition of pixels [19].…”

Section: Structure Design Of Top-down Feature Fusionmentioning

confidence: 99%

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

Li¹

2021

Security and Communication Networks

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In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.

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“…In the formula, conv3(•) is the convolution operation of the feature graph and + is the polar addition of pixels [19].…”

Section: Structure Design Of Top-down Feature Fusionmentioning

confidence: 99%

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

Li¹

2021

Security and Communication Networks

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“…Fatigue and sleepiness detection have particularly benefited from the rapid improvement of CNN and RNN structures. However, although face classification accuracy has already reached a high level, precisely determining whether the subject is tired remains a challenge, especially when processing video data [55].…”

Section: Fatigue Detection Based On Mixed Behaviour Characteristicsmentioning

confidence: 99%

Real time detection of driver fatigue based on CNN‐LSTM

Liu

et al. 2021

IET Image Processing

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Fatigue driving is one of the main causes of traffic accidents. In order to solve this problem, a new Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) based real-time driver fatigue detection method is proposed. First of all, using simple linear clustering algorithm (SLIC), the driver's image is divided into super pixels of uniform size, which are used as input of CNN, and CNN is trained to automatically learn the features of eyes and mouth contained in the image, and then the location and area of eyes and mouth are obtained by using the trained CNN. On this basis, the eye feature parameter Perclos, mouth feature parameter MClosed and face orientation feature parameter Phdown are extracted, and the above feature parameters on the continuous time series and steering wheel angle feature parameter SA are taken as the input of LSTM, and the fatigue level is taken as the output to detect the fatigue state of the driver in real time. Experimental data shows that this method can not only overcome the influence of illumination, background, angle and individual differences, but also the accuracy of detection can reach 99.78%, and the average detection time is 16.94 ms/frame.

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“…InceptionV3 is an implementation of GoogLeNet, its ability to deconstruct these features into smaller convolution sections (Zhao et al 2020). Its network can be efficiently decomposed into small convolution kernels, which greatly reduces the number of parameters of the model and the chance of overfitting (Liu et al 2020).…”

Section: Inceptionv3mentioning

confidence: 99%

Comparative Study of Ensemble Models of Deep Convolutional Neural Networks for Crop Pests Classification

Luo

Wang

et al. 2021

Preprint

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Pest infestations on wheat, corn, soybean, and other crops can cause substantial losses to their yield. Early diagnosis and automatic classification of various insect pest categories are of considerable importance for accurate and intelligent pest control. However, given the wide variety of crop pests and the high degree of resemblance between certain pest species, the automatic classification of pests can be very challenging. To improve the classification accuracy on publicly available D0 dataset with 40 classes, this paper compares studies on the use of ensemble models for crop pests classification. First, six basic learning models as Xception, InceptionV3, Vgg16, Vgg19, Resnet50, MobileNetV2 are trained on D0 dataset. Then, three models with the best classification performance are selected. Finally, the ensemble models, i.e, linear ensemble named SAEnsemble and nonlinear ensemble SBPEnsemble, are designed to combine the basic learning models for crop pests classification. The accuracies of SAEnsemble and SBPEnsemble improved by 0.85% and 1.49% respectively compared to basic learning model with the highest accuracy. Comparison of the two proposed ensemble models show that they have different performance under different condition. In terms of performance metrics, SBPEnsemble giving accuracy of classification at 96.18%, is more competitive than SAEnsemble.

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Intelligent Recognition of Fatigue and Sleepiness Based on InceptionV3-LSTM via Multi-Feature Fusion

Cited by 34 publications

References 44 publications

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

Real time detection of driver fatigue based on CNN‐LSTM

Comparative Study of Ensemble Models of Deep Convolutional Neural Networks for Crop Pests Classification

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