A new hybrid deep learning model for human action recognition

Jaouedi, Neziha; Boujnah, Noureddine; Bouhlel, Med Salim

doi:10.1016/j.jksuci.2019.09.004

Cited by 133 publications

(82 citation statements)

References 25 publications

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“…UCF Sports Actions dataset (%) Physical Sports Movements [27] 86.67 HOIRM feature fusion [28] 88.25 Hybrid deep learning model [29] 89.01 Proposed method 90.91 A comparison of overall results shows that the proposed method achieved a significant improvement with recognition results as high as 89.09% and 88.26% over other methods as shown in Table 5. Table 5.…”

Section: Methodsmentioning

confidence: 99%

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Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

2020

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Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.

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Section: Methodsmentioning

confidence: 99%

“…Physical Sports Movements [27] 86.67 HOIRM feature fusion [28] 88.25 Hybrid deep learning model [29] 89.01…”

Section: Ucf Sports Actions Dataset (%)mentioning

confidence: 99%

Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

2020

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“…Each activity is presented as a short video of 3 s. To properly recognize human activities throughout the frames of each mini video, we used Inception V3 to extract the visual characteristics of a person in each frame. Moreover, to locate the moving person in a video sequence, we used a linear Kalman filter [38].…”

Section: Activity Recognitionmentioning

confidence: 99%

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

Jaouedi¹,

Perales

Buades

et al. 2020

Sensors

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The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

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“…Zeng et al, [ 24 ] proposed an approach to automatically extract discriminative features for recognising activity, based on CNN and based on the data from the mobile sensors embedded in smart phones. Jaouedi et al [ 25 ] presented an approach based on the analysis of video content where features are based on all visual characteristic of each frame of a video sequence, using the recurrent neural networks model with the gated recurrent unit.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

Patalas‐Maliszewska

Halikowski

2020

Sensors

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Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model’s accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee’s activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.

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A new hybrid deep learning model for human action recognition

Cited by 133 publications

References 25 publications

Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

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