A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition

Sagayam, K. Martin; Hemanth, D. Jude

doi:10.1111/coin.12188

Cited by 27 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure 2 appears the training case that was implemented. Later in the training procedure of demonstrating completed, the model testing prepare was enforced to conclude the precision of the prepared mass [15]. In Figure 3 shows the hand gesture for "sorry" which is extracted from the camera [16].…”

Section: Methodsmentioning

confidence: 99%

Sign Language Translator Using YOLO Algorithm

Bhavadharshini¹,

J²,

Kamali³

et al. 2021

Advances in Parallel Computing

View full text Add to dashboard Cite

Sign language is a terminology that encloses a motion of hand gestures which is an environment for the auditory impairment, individual (deaf or dumb) to deal with others. Nevertheless, so as to impart with the hearing impaired individual, the communicator obtains to acquire acquaintance in sign language. As follows is frequent to make undoubted that the message provided by the hearing impaired person acknowledged. This implemented system propounds an implementation of real time American Sign Language perception in Convolutional Neural Network (CNN) with the support of You Only Look Once version (YOLO) algorithm. The algorithm initially executes data acquisition, subsequently the pre-processing of gestures and are conducted to trace hand movement utilize a combinational algorithm.

show abstract

Section: Methodsmentioning

confidence: 99%

Sign Language Translator Using YOLO Algorithm

Bhavadharshini¹,

J²,

Kamali³

et al. 2021

Advances in Parallel Computing

View full text Add to dashboard Cite

show abstract

“…Так, для анализа динамических жестов рук применяются другие методы. В работе [30] признаки из изображений жестов рук извлекались с помощью алгоритма ускоренных надежных функции (англ. Speeded Up Robust Feature, сокращенно SURF).…”

Section: системы основанные на компьютерном зренииunclassified

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Ryumin¹,

Kagirov²

2021

Manned Spaceflight

View full text Add to dashboard Cite

In this paper, hardware and software solutions addressed to automatic gesture recognition are considered. Trends in image analysis in the current computer vision-based approaches are analysed. Each of the considered approaches was addressed, in order to reveal their advantages and drawbacks. Research papers on the usability of gesture interfaces were reviewed. It was revealed that sensor-based systems, being quite accurate and demonstrating high speed of recognition, have limited application due to the specificity of devices (gloves, suit) and their relatively narrow distribution. At the same time, computer vision-based approaches can be successfully applied only when problems of occlusions and datasets are solved. The results obtained can be used for designing training systems.

show abstract

“…An actions primitive [2,3] in terms of temporal sequence of a single person interaction (not more number of objects, humans) is considered for this approach. As it works based on space-time interest points (STIPs) [4] in which the variations among the image vectors is shown in terms of both time and space. Meanwhile, another method based on local descriptors such as histogram of oriented gradients (HOG) [5] and histogram of optical flow (HOF) is used for recognizing object.…”

Section: Vision Based Human Activity Recognitionmentioning

confidence: 99%

Distinct Actions Classification Using Human Action Tracker Technique in Sports Videos

Kanimozhi¹,

S²,

Mythili³

2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

Recognizing human action in sports is difficult task as various sequences of activities involved in every scene. Identifying each action individually without overlapping of movements is a tedious process due to continuous change of frames within short duration. So proper tracking of human movements for each action is important. Hence new structure-based human action recognition and tracker technique (HART) is proposed. It uses joint trajectory images and visual feature to design each human action. At first, a structural based method employed to extract human skeleton data points from RGB (Red Green Blue) videos. Next, a Multitude Object Tracker (MOT) is proposed which uses the trajectory of human skeleton joints in an image space for identification of actions. Then, Histogram of Oriented Gradients (HOG) combined with Support Vector Machine (SVM) is applied to extract physical body shape and action information. Finally, the action label and interconnected keypoints in humans is jointly detected as end result. The proposed HART technique effectively performed well with the accuracy of about 82% over the other activity recognition methods.

show abstract

A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition

Cited by 27 publications

References 27 publications

Sign Language Translator Using YOLO Algorithm

Sign Language Translator Using YOLO Algorithm

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Distinct Actions Classification Using Human Action Tracker Technique in Sports Videos

Contact Info

Product

Resources

About