Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

Kumar, Kaushal; Kishore, P. V. V.

doi:10.11591/ijece.v7i5.pp2537-2546

Cited by 28 publications

(7 citation statements)

References 9 publications

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“…To calculate d, newton-raphson approximation is used as in (8). It is derived from ( 6) and ( 7) that are formula for x0 and x1 on a newton-raphson digital blocks.…”

Section: Block Normalization Using Newton-raphson Methodsmentioning

confidence: 99%

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

Adiono

Kasan

2023

IJ-AI

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In respect of the accuracy, one of the well-known techniques for human detection is the histogram-oriented gradients (HOG) method. Unfortunately, the HOG feature calculation is highly complex and computationally intensive. Thus, in this research, we aim to achieve a resource-efficient and low-power HOG hardware architecture while maintaining its high frame-rate performance for real-time processing. A hardware architecture for human detection in 2D images using simplified HOG algorithm was introduced in this paper. To increase the frame-rate, we simplify the HOG computation while maintaining the detection quality. In the hardware architecture, we design a cell-based processing method instead of a window-based method. Moreover, 64 parallel and pipeline architectures were used to increase the processing speed. Our pipeline architecture can significantly reduce memory bandwidth and avoid any external memory utilization. an altera field programmable gate arrays (FPGA) E2-115 was employed to evaluate the design. The evaluation results show that our design achieves performance up to 86.51 frame rate per second (Fps) with a relatively low operating frequency (27 MHz). It consumes 48,360 logic elements (LEs) and 4,363 registers. The performance test results reveal that the proposed solution exhibits a trade-off between Fps, clock frequency, the use of registers, and Fps-to-clock ratio.

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“…To calculate d, newton-raphson approximation is used as in (8). It is derived from ( 6) and ( 7) that are formula for x0 and x1 on a newton-raphson digital blocks.…”

Section: Block Normalization Using Newton-raphson Methodsmentioning

confidence: 99%

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

Adiono

Kasan

2023

IJ-AI

View full text Add to dashboard Cite

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“…The research article by authors in [8] focuses on feature extraction and then uses AdaBoost multi-class classifier [9] on multi-feature fusion. The authors in [10] give another pioneer contribution specific to recognizing classical dance forms and mudras using an imagery dataset of hand mudras from different classical dance forms. The support vector machine (SVM) classifier is given histogram of oriented (HOG) features as input for classification.…”

Section: Literature Surveymentioning

confidence: 99%

“…AdaBoost multi feature fusion classification [10] Histogram of oriented (HOG) features of hand mudras SVM classifier [17] Pipeline-joints identification, patches centered around regions important with respect to movement, and this forms a hierarchical dance pose descriptor.…”

Section: Literature Surveymentioning

confidence: 99%

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An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

Jain

Bansal

Virmani³

et al. 2021

Applied Sciences

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Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches.Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911.

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“…Because a single image feature does not completely use picture information, several researchers have recovered multiple features in different situations to represent visual information more comprehensively. However, because the classifier frequently receives a single eigenvector as input, it must merge several singular eigenvectors to produce a vector [4]. When utilizing the approach of intentionally constructing picture features, however, fewer are obtained and classifier parameters are lowered.…”

Section: Introductionmentioning

confidence: 99%

Health System for Exercise Rehabilitation Detection Retina Images and IoT Blockchain

Sriman,

et al. 2023

ijmst

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Smart Healthcare which is based on deep learning is becoming increasingly popular because of its practical applications and has grown popular after its incorporation with IoT. Degenerative eye disease is the main factor of blindness in people of working age. Asian nations with large populations, like India and China, are on the edge of a diabetes epidemic. In terms of medical screening and diagnosis, a large number of diabetes patients posed a huge problem for skilled clinicians. The idea is to employ deep learning algorithms to detect blind spots in the eye and estimate the severity of the stage. We present an optimum approach for detecting blindness in retinal images based on recently released pre-trained EfficientNet models, as well as a comparative assessment of many innovative neural network models, in this study. On a benchmark dataset of retina images obtained by diagnostic imaging at various imaging phases, our EfficientNet-B5-based model assessment performs better than CNN and ResNet50 models.

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Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

Cited by 28 publications

References 9 publications

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

A high frame-rate of cell-based histogram-oriented gradients human detector architecture implemented in field programmable gate arrays

An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

Health System for Exercise Rehabilitation Detection Retina Images and IoT Blockchain

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