Robust Human Action Recognition Using AREI Features and Trajectory Analysis from Silhouette Image Sequence

Maity, Satyabrata; Chakrabarti, Amlan; Bhattacharjee, Debotosh

doi:10.1080/03772063.2017.1409085

Cited by 11 publications

(11 citation statements)

References 17 publications

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“…e K(x, y) in formula ( 7) is also called, which can map samples from the linearly inseparable low-dimensional space to the high-dimensional space [23]. After determining the kernel function type [24], above kernel functions, respectively, have the following expressions:…”

Section: Use Support Vector Machine Model For Actionmentioning

confidence: 99%

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Zhang

2021

Mobile Information Systems

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As the world's largest sport, football has affected a wide area and a large number of participants and had a great impact on political economy and culture, which has become the best embodiment of the social function of football. Throughout the experience of football in developed countries in the world, the stable development of youth football is the best way to improve the level of football in a country, and the Chinese Football Association has invested more energy in professional leagues and national teams. The development of youth campus football is basically in a state of no management. Therefore, people gradually realize the concept that “football should serve education.” In order to solve the problem of football players' lack of exercise in multiple subjects, it is particularly important to design systems and make plans for their respective physical characteristics. After the failure of various important competitions, the Chinese national football team reflected on the specific factors of backwardness. Under today's system, no one manages the specific development of youth campus football. Especially for young people, training programs that adapt to their individual characteristics should be formulated according to their growth stage and physical characteristics. It can effectively improve the efficiency of football teaching and training (FTT) by managing football players' training information and coaches' teaching information in an intelligent and informatized way. The different sports in which athletes participate in training are identified through the motion recognition layer. The data generated during the entire exercise process, including exercise time, number of exercises, score settlement, and other data, are stored, and the data are finally uploaded to the server, to carry out scientific analysis and management and generate sports training prescriptions in line with their own characteristics. This paper proposes research methods based on the intelligent integrated system of FTT, including literature retrieval, questionnaire survey, training empirical method, comparative analysis method, interview method, and support vector machine model for action recognition, which are used in football teaching and the design experiment of the intelligent integrated system for training; the overall architecture design of the football teaching intelligent integration system and the specific design of the football teaching intelligent integration system are proposed. The experimental results of this article show that 90.70% of players like the teaching mode of intelligent FTT, and the intelligent FTT system can help improve the enthusiasm of players in training and learning.

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Section: Use Support Vector Machine Model For Actionmentioning

confidence: 99%

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Zhang

2021

Mobile Information Systems

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“…For the abnormal behavior recognition of the same posture, the accuracy always fluctuates between 99% and 100%. In contrast, in the process of different posture recognition, the recognition accuracy of literature [10] algorithm, literature [11] algorithm, literature [12] algorithm, literature 5 Wireless Communications and Mobile Computing [13] algorithm, and literature [14] algorithm fluctuates between 90% and 98.5%, and the recognition accuracy of the same posture is always lower than 96%. The recognition accuracy of the proposed algorithm is significantly higher than that of the other five algorithms.…”

Section: Resultsmentioning

confidence: 94%

“…The proposed algorithm is compared with the algorithm in literature [10][11][12][13][14]. The comparison results of the accuracy and recall of the six algorithms for the abnormal recognition of multipose motion behavior in the experimental data set are shown in Figures 4 and 5.…”

Section: Resultsmentioning

confidence: 99%

“…According to three types of postfusion techniques, namely, maximum average and multiplication, the final prediction of single stream fraction is made. Literature [12] proposed an effective method for human motion recognition (HAR) from silhouette image sequences in video. The effectiveness of this method lies in feature extraction and action classification.…”

Section: Related Workmentioning

confidence: 99%

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Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Zhang

Ren

et al. 2022

Wireless Communications and Mobile Computing

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Human multipose motion behavior is similar; there are many actions. However, it is difficult to recognize abnormal behavior. The existing human motion behavior anomaly recognition methods have the problems of low accuracy and being time-consuming. Therefore, an anomaly recognition method of human multipose motion behavior using Generative Adversarial Network (GAN) is proposed. The Gauss model is used to segment the human multipose motion behavior image, and the image foreground of the segmentation result is the human motion target detection result. The Shi-Tomasi algorithm is selected to extract contour feature points from human motion object detection results. The extracted contour features are set as hidden space random variables and input into the GAN. The GAN uses the generator and discriminator to recognize the multipose human motion behavior and determine whether the multipose human motion behavior is abnormal. The results show that the proposed algorithm can accurately recognize abnormal human multipose motion behavior, the recognition accuracy is higher than 99%, and the average recognition time is less than 200 ms. The shadow removal effect of the foreground image obtained by the proposed algorithm can realize the accurate recognition of human multipose motion behavior abnormalities and provide a reliable basis for research in related fields.

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“…In [41] measurements start after the patient has walked on to a starting platform located before the force platform used for real data capturing, while in [37] preprocessing cuts noise. Especially in Silhouette analysis, preprocessing techniques are used to remove noise, background information and in some image processing cases, overlapping obstacles [21], [29], [30], [32], [33], [38], [39], [40], [42].…”

Section: B Preprocessingmentioning

confidence: 99%

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

2020

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Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation. INDEX TERMS pose-estimation, computer vision, computer aided diagnosis, gait analysis, machine learning, early neurodegenerative diseases assessment, kinematic theory of rapid human movements, sigma-lognormal

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Robust Human Action Recognition Using AREI Features and Trajectory Analysis from Silhouette Image Sequence

Cited by 11 publications

References 17 publications

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Computer Aided Teaching System Based on Artificial Intelligence in Football Teaching and Training

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

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