scholarly journals Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Drones ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 82 ◽  
Author(s):  
Asanka G. Perera ◽  
Yee Wei Law ◽  
Javaan Chahl
Keyword(s):  
Action Recognition ◽  
Situational Awareness ◽  
Human Action Recognition ◽  
Human Action ◽  
Machine Learning Techniques ◽  
High Definition ◽  
Human Actions ◽  
Research Areas ◽  
Indoor Scenes ◽  
High Level

Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. A free flying drone was used to record 13 dynamic human actions. The dataset contains 240 high-definition video clips consisting of 66,919 frames. All of the videos were recorded from low-altitude and at low speed to capture the maximum human pose details with relatively high resolution. This dataset should be useful to many research areas, including action recognition, surveillance, situational awareness, and gait analysis. To test the dataset, we evaluated the dataset with a pose-based convolutional neural network (P-CNN) and high-level pose feature (HLPF) descriptors. The overall baseline action recognition accuracy calculated using P-CNN was 75.92%.

Silhouette Pose Feature-Based Human Action Classification Using Capsule Network

2021 ◽  
Vol 14 (2) ◽  
pp. 106-124
Author(s):  
A. F. M. Saifuddin Saif ◽  
Md. Akib Shahriar Khan ◽  
Abir Mohammad Hadi ◽  
Rahul Proshad Karmoker ◽  
Joy Julian Gomes
Keyword(s):  
Action Recognition ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Machine Learning Techniques ◽  
Camera Motion ◽  
Human Actions ◽  
Learning Techniques ◽  
Standing Up ◽  
Feature Based

Recent years have seen a rise in the use of various machine learning techniques in computer vision, particularly in posing feature-based human action recognition which includes convolutional neural networks (CNN) and recurrent neural network (RNN). CNN-based methods are useful in recognizing human actions for combined motions (i.e., standing up, hand shaking, walking). However, in case of uncertainty of camera motion, occlusion, and multiple people, CNN suppresses important feature information and is not efficient enough to recognize variations for human action. Besides, RNN with long short-term memory (LSTM) requires more computational power to retain memories to classify human actions. This research proposes an extended framework based on capsule network using silhouette pose features to recognize human actions. Proposed extended framework achieved high accuracy of 95.64% which is higher than previous research methodology. Extensive experimental validation of the proposed extended framework reveals efficiency which is expected to contribute significantly in action recognition research.

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

scholarly journals A Hierarchical Learning Approach for Human Action Recognition

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4946
Author(s):  
Nicolas Lemieux ◽  
Rita Noumeir
Keyword(s):  
Action Recognition ◽  
Inertial Sensor ◽  
Computational Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Practical Applications ◽  
Learning Framework ◽  
Sensor Signals ◽  
High Level ◽  
Computational Resources

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces. For that purpose, we propose a new learning framework built around a 1D-CNN architecture, which we validated by achieving very competitive results on the publicly available UTD-MHAD dataset. Moreover, the proposed method provides some answers to two of the greatest challenges currently faced by action recognition algorithms, which are (1) the recognition of high-level activities and (2) the reduction of their computational cost in order to make them accessible to embedded devices. Finally, this paper also investigates the tractability of the features throughout the proposed framework, both in time and duration, as we believe it could play an important role in future works in order to make the solution more intelligible, hardware-friendly and accurate.

Recognition of Inter-Class Variation of Human Actions in Sports Video

2021 ◽  
Author(s):  
Akila.K
Keyword(s):  
Action Recognition ◽  
Wavelet Packet ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Human Actions ◽  
Linear Discriminant ◽  
Novel Approach ◽  
Current Events ◽  
Rule Set

Abstract Background: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category.Objective: This paper focuses on a specific issue related to inter-class variation in Human Action Recognition.Approach: To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features are analyzed and discriminated by Deterministic Normalized - Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set..Conclusion: Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation

scholarly journals Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1589
Author(s):  
Zeyuan Hu ◽  
Eung-Joo Lee
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Great Success ◽  
Semantic Features ◽  
Convolutional Networks ◽  
Temporal Correlations ◽  
Semantic Relevance ◽  
High Level ◽  
Relationship Of

Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition. Our goal is to explore the best correlation and determine high-level semantic features. First, a multiscale dynamic aggregate GCN module is used to capture important semantic information and to establish dependence relationships for different bone nodes. Second, the higher level semantic feature is further refined, and the semantic relevance is emphasized through a dual attention guidance module. In addition, we exploit the relationship of joints hierarchically and the spatial temporal correlations through two modules. Experiments with the DAG-GCN method result in good performance on the NTU-60-RGB+D and NTU-120-RGB+D datasets. The accuracy is 95.76% and 90.01%, respectively, for the cross (X)-View and X-Subon the NTU60dataset.

scholarly journals Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 49
Author(s):  
Nusrat Tasnim ◽  
Md. Mahbubul Islam ◽  
Joong-Hwan Baek
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Dimensional Space ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Machine Interaction ◽  
Human Actions ◽  
3 Dimensional ◽  
3D Skeleton ◽  
Color Depth

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

scholarly journals Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1993
Author(s):  
Malik Ali Gul ◽  
Muhammad Haroon Yousaf ◽  
Shah Nawaz ◽  
Zaka Ur Rehman ◽  
HyungWon Kim
Keyword(s):  
Activity Recognition ◽  
Action Recognition ◽  
Human Activity ◽  
Patient Monitoring ◽  
Human Action Recognition ◽  
Confidence Score ◽  
Human Action ◽  
Human Activity Recognition ◽  
Video Sequences ◽  
Human Actions

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.

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