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نتیجه جستجو - Human action recognition

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 An overview of Human Action Recognition in sports based on Computer Vision
مروری بر تشخیص کنش انسانی در ورزش بر اساس بینایی کامپیوتری-2022
Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and tennis to detect players and recognize their actions and teams activities during training, matches, warm-ups, or competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the actions duration, and identify the action type. The main idea of HAR in sports is to monitor a players performance, that is, to detect the player, track their movements, recognize the performed action, compare various actions, compare different kinds and skills of acting performances, or make automatic statistical analysis. As an action that can occur in the sports field refers to a set of physical movements performed by a player in order to complete a task using their body or interacting with objects or other persons, actions can be of different complexity. Because of that, a novel systematization of actions based on complexity and level of performance and interactions is proposed.
The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose.
keywords: یادگیری ماشین | تشخیص عمل انسانی | سیستم سازی اقدام | مجموعه داده های ورزشی | شناخت کنش انسان در ورزش | ورزش | Machine learning | Human Action Recognition | Action systematization | Sports dataset | Human action recognition in sports | Sport
مقاله انگلیسی
2 Action recognition of dance video learning based on embedded system and computer vision image
تشخیص عمل یادگیری ویدئویی رقص بر اساس سیستم تعبیه شده و تصویر بینایی ماشین-2021
Extraction and unfettered online / offline video sequence to identify complex human activity is computer vision a challenging task. To presents the classification of Indian classical dance moves using the powerful features of embedded system tools: Field Programmable Gate Array (FPGA). In this work, the Indian classical dance video for human action recognition is, YouTube data from offline and online control audio and video recordings of live performances carried out. Handprint create offline data with ten different themes familiar dance of 200 m / from various Indian classical dance forms in the context of a variety of poses. Online data collection dance ten different subjects from YouTube. Each dance posture is occupied 60 or video in both cases. FPGA training and 8 different sample dimensions, each performed by a plurality of sets of subject. The remaining two samples for testing the trained FPGA. Different FPGA architecture design, and with our test data in order to obtain better recognition accuracy. Compared the report on the same data set and other classification model to achieve a 90% recognition rate.
Keywords: Field-programmable gate array (FPGA) | Learning action recognition | Embedded system tool
مقاله انگلیسی
3 Vision-assisted recognition of stereotype behaviors for early diagnosis of Autism Spectrum Disorders
تشخیص رفتارهای کلیشه ای برای تشخیص زودهنگام اختلالات طیف اوتیسم با کمک بینایی ماشین-2021
Medical diagnosis supported by computer-assisted technologies is getting more popularity and acceptance among medical society. In this paper, we propose a non-intrusive vision-assisted method based on human action recognition to facilitate the diagnosis of Autism Spectrum Disorder (ASD). We collected a novel and comprehensive video dataset f the most distinctive Stereotype actions of this disorder with the assistance of professional clinicians. Several frameworks as a function of different input modalities were developed and used to produce extensive baseline results. Various local descriptors, which are commonly used within the Bag-of-Visual-Words approach, were tested with Multi-layer Perceptron (MLP), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVM) classifiers for recognizing ASD associated behaviors. Additionally, we developed a framework that first receives articulated pose-based skeleton sequences as input and follows an LSTM network to learn the temporal evolution of the poses. Finally, obtained results were compared with two fine-tuned deep neural networks: ConvLSTM and 3DCNN. The results revealed that the Histogram of Optical Flow (HOF) descriptor achieves the best results when used with MLP classifier. The promising baseline results also confirmed that an action-recognition-based system can be potentially used to assist clinicians to provide a reliable, accurate, and timely diagnosis of ASD disorder.© 2021 Elsevier B.V. All rights reserved.
Keywords: Action recognition | Autism Spectrum Disorder | Patient monitoring | Bag-of-visual-words | Convolutional neural networks
مقاله انگلیسی
4 Capturing causality and bias in human action recognition
ثبت علیت و سوگیری در تشخیص عمل انسان-2021
Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distilla- tion for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect. Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved.
Keywords: Machine learning | Decision support | Human action recognition | Machine reasoning | Belief networks
مقاله انگلیسی
5 A new hybrid deep learning model for human action recognition
یک مدل جدید یادگیری عمیق ترکیبی برای شناخت عملکرد انسان-2019
Human behavior has been always an important factor in social communication. The human activity and action recognition are all clues that facilitate the analysis of human behavior. Human action recognition is an important challenge in a variety of application including human-computer interaction and intelligent video surveillance to enhance security in different domains. The evaluation algorithm relies on the proper extraction and the learning data. The success of the deep learning led to many imposing results in several contexts that include neural network. Here the emergence of Gated Recurrent Neural Networks with increased computation powers is being adopted for sequential data and video classification. However, to have an efficient classifier for assigning the class label, it is very necessary to have a strong features vector. Features are the most important information in each data. Indeed, features extraction can influence on the performance of the algorithm and the computation complexity. This paper proposes a novel approach for human action recognition based on hybrid deep learning model. The proposed approach is evaluated on the challenging UCF Sports, UCF101 and KTH datasets. An average of 96.3% is obtained when we have tested on KTH dataset
Keywords: Deep learning | Recurrent Neural Networks | Gated Recurrent Unit | Video classification | Motion detection
مقاله انگلیسی
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