Group activity recognition with an interaction force based on low‐level features

Wateosot, Chonthisa; Suvonvorn, Nikom

doi:10.1002/tee.22901

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…This can lead to an ambiguous boundary between the normal and abnormal events, and requires the model to learn only unique and discriminative features for classification [135]. Moreover, the tracking of multisubject interactions in a group of people is a challenging problem that requires a model to have the ability to capture spatiotemporal information from subjects [136].…”

Section: Challengesmentioning

confidence: 99%

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Duong

Hoang

2023

Sensors

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Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.

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Section: Challengesmentioning

confidence: 99%

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Duong

Hoang

2023

Sensors

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“…Advances in Mathematical Physics the data of ordinary trainees with the data of high-level athletes. Further, the literature [11] also selects the videos of the best athletes with the most similar scores to the trainees for each sport characteristic and recommends them to the trainees as learning targets. In addition, the literature [12] provides a conceptualization on how to better monitor and analyze the data, respectively, including how to choose the type of data to be monitored in sports and how to process the data after monitoring, which provides a guideline for digitization of general sports training.…”

Section: Related Workmentioning

confidence: 99%

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Zhang

Gui

et al. 2021

Advances in Mathematical Physics

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Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.

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Multi-user activity recognition: Challenges and opportunities

Gravina

et al. 2020

Information Fusion

112

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Group activity recognition with an interaction force based on low‐level features

Cited by 7 publications

References 32 publications

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Multi-user activity recognition: Challenges and opportunities

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