Human action identification and search in video files

Kundid, Mirela; Galić, Irena; Vasić, Daniel

doi:10.1109/elmar.2015.7334534

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…However, aimed at a huge video database, it is a very lengthy procedure to allot meta-data manually to videos via watching every video 7 . With the incrementing power of computers, advanced methodologies depend on designing actions by recording the movement of diverse perceptions aimed at acquiring an action design that must be recognized 8 . The target of deploying machine learning (ML) aimed at HAR is to know the actions that have been executed in the video.…”

Section: Introductionmentioning

confidence: 99%

“…7 With the incrementing power of computers, advanced methodologies depend on designing actions by recording the movement of diverse perceptions aimed at acquiring an action design that must be recognized. 8 The target of deploying machine learning (ML) aimed at HAR is to know the actions that have been executed in the video. The deep learning (DL) methodologies have efficient performance analogized to the conventional ML methodologies.…”

Section: Introductionmentioning

confidence: 99%

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Survey on artificial intelligence-based human action recognition in video sequences

Kumar

2023

Opt. Eng.

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In the artificial intelligence domain, human action recognition (HAR) has evolved as one of the major active research topics as a reason of diverse applications, namely video surveillance. The extensive variation types amidst routine human activities make the recognition procedure much more intricate. As a reason for an increment in the cameras' usage, automated systems are essential to categorize activities, such as these utilizing computationally intelligent methodologies, namely machine learning and deep learning (DL). This paper's organization covers all the characteristics of the HAR's general framework. This work talks about the public dataset's characteristics utilized that are aimed at HAR. After that, it summarizes and classifies the recently published research enhancement under the common framework. The merits and demerits of dimensionality reduction, action representation, and as well as action analysis methodologies are offered. After that, the outcomes exhibit the DL methodologies' performance via graphs. This article gives a clear vision of what we can explore in the activity recognition area. The work talks about HAR's challenges and its future directions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey on artificial intelligence-based human action recognition in video sequences

Kumar

2023

Opt. Eng.

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Review on Real Time Approach to Identify a Person based on Hand drawn Sketch Using Deep Learning

Khan,

Kumar

2023

2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)

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Comparison and Evaluation of Machine Learning-Based Classification of Hand Gestures Captured by Inertial Sensors

et al. 2022

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Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

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Human action identification and search in video files

Cited by 3 publications

References 13 publications

Survey on artificial intelligence-based human action recognition in video sequences

Survey on artificial intelligence-based human action recognition in video sequences

Review on Real Time Approach to Identify a Person based on Hand drawn Sketch Using Deep Learning

Comparison and Evaluation of Machine Learning-Based Classification of Hand Gestures Captured by Inertial Sensors

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