A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION

Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistics based methodologies, syntactic methodologies, and description based methodologies for hierarchical recognition is examined in the paper.

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Robust video content analysis schemes for human action recognition

Science Progress ◽

10.1177/00368504211005480 ◽

2021 ◽

Vol 104 (2) ◽

pp. 003685042110054

Author(s):

Cherry A. Aly ◽

Fazly S. Abas ◽

Goh H. Ann

Keyword(s):

Action Recognition ◽

High Performance ◽

General Scheme ◽

Human Action Recognition ◽

Human Action ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

Frame Rate ◽

Camera Motion ◽

Video Content Analysis

Introduction: Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval. Objectives: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms. Methods: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification. Results: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann. Conclusion: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

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Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2019040103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 34-47 ◽

Cited By ~ 1

Author(s):

Bagavathi Lakshmi ◽

S.Parthasarathy

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Action Recognition ◽

Human Activity ◽

Nearest Neighbor ◽

Human Action Recognition ◽

Human Action ◽

Human Activity Recognition ◽

Machine Learning Algorithms ◽

Support Vector

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

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Human Action Recognition using STIP Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5482.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 878-883

Keyword(s):

Action Recognition ◽

Median Filter ◽

Human Action Recognition ◽

Human Action ◽

Video Frames ◽

Specificity And Sensitivity ◽

Human Computer Interfaces ◽

Classifier Performance ◽

Computer Vision Technology ◽

Correct Category

The activities of human can be classified into human actions, interactions, object- human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are Surveillance environment systems, healthcare systems, Military, patient monitoring systems (PMS), etc., that involve interactions between electronic devices such as human-computer interfaces with persons. Initially collected the videos containing actions or interactions were performed by the humans. The given input videos were converted into number of frames and then these frames were undergone preprocessing stage using by applying median filter. The median filter identifies the noises present in the frame and then which replaces the noise by the median of the neighboring pixels. Through frames desired features were extracted. The recognize of action present in the person of the video using these extracted features. There are three spatial temporal interest point (STIP) techniques such as Harris SPIT, Gabour SPIT and HOG SPIT were used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the Accuracy, Sensitivity and Specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represents how exactly the classifier categorizes it’s features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category

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Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

Frontiers in Psychology ◽

10.3389/fpsyg.2021.663359 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiatian Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition Accuracy ◽

Three Dimensional ◽

Human Action Recognition ◽

Human Movement ◽

Human Action ◽

Recognition Algorithm ◽

Basketball Players ◽

Movement Recognition

In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.

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Human action recognition using simple geometric features and a finite state machine

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

Author(s):

Damian Dudzńiski ◽

Tomasz Kryjak ◽

Zbigniew Mikrut

Keyword(s):

Action Recognition ◽

Finite State Machine ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Video Stream ◽

State Machine ◽

Recognition Algorithm ◽

Finite State ◽

Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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