A spatiotemporal approach for vision-based hand gesture recognition using Hough transform and neural network

Patil, Anjali R.; Subbaraman, Shaila

doi:10.1007/s11760-018-1370-1

Cited by 22 publications

(7 citation statements)

References 22 publications

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“…The adhesive conformability to complicated and arbitrary surfaces demonstrates that the strain sensor offers wearable gadgets that provide a wide range of application possibilities. Patil et al 18 proposed the spatiotemporal method of vision‐based recognition of hand gestures utilizing Hough transform and neural networks. The significant objective of this article was to detect the hand gestures utilizing Hough transforms based on the spatiotemporal technique and the artificial neural networks used for identification.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Palanisamy¹,

Thangaswamy²

2022

Concurrency and Computation

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Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human-computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state-of-art systems mainly used computer vision-based techniques that utilize both the motion sensor and camera to capture the hand gestures in real-time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi-scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN-LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi-block local binary pattern (MB-LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN-LSTM network is applied in the two-benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset-II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN-LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.

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Section: Literature Surveymentioning

confidence: 99%

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Palanisamy¹,

Thangaswamy²

2022

Concurrency and Computation

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“…Based on the research of [5], the approaches used in DHGR can be gathered into two main categories according to the types of their inputs: image-based approaches [6,7] and skeleton-based approaches [8,9]. Generally, the former conducts pattern recognition tasks with images containing human hands captured by RGB camera, and hand skeletons made up with threedimensional (3D) coordinates of each joint are utilized for classification in the latter approaches.…”

Section: Introductionmentioning

confidence: 99%

MVHANet: multi-view hierarchical aggregation network for skeleton-based hand gesture recognition

Liu

Duan

et al. 2023

SIViP

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Skeleton-based gesture recognition (SHGR) is a very challenging task due to the complex articulated topology of hands.Previous works often learn hand characteristics from a single observation viewpoint. However, the spatial structure of the hand skeletons cannot be investigated explicitly, and various context information hidden in multiple viewpoints is disregarded. To resolve this issue, we propose a novel multi-view hierarchical aggregation network (MVHANet) for SHGR, which is composed of three parts. Firstly, two-dimensional non-uniform spatial sampling (2DNUSS), a novel strategy forming extrinsic parameter distributions of virtual cameras, is presented to enumerate viewpoints to observe hand skeletons from reasonable directions. Afterwards, we adopt coordinate transformation to generate multi-view hand skeletons and employ a multi-branch convolutional neural networks (CNNs) to further extract the multi-view features of hand skeletons.Furthermore, we exploit a novel hierarchical aggregation network including hierarchical attention architecture (HAA) and global context modeling (GCM) to fuse the multi-view features. Specifically, the HAA is composed of view level and group level for learning the local correlation and discriminative weights of multiple views; GCM is aimed to capture the global information of all the views from each level. Finally, the outputs of HAA and GCM are combined for hand gesture classification. In this way, we fully leverage the multi-view information of hand skeletons and improve the performance of recognition. We conduct extensive experiments on publicly available SHREC'17 Track dataset, DHG-14/28 dataset and First-Person Dynamic Hand Actions dataset. Experimental results demonstrate that our work can be competitive with the state-of-the-art methods.

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“…In the literature, many researchers have applied hand-crafted feature extraction techniques such as shape descriptors, spatiotemporal features [ 16 ], and the recognition of hand gestures. However, these features have performed well in a specific environment while performance has degraded in varied conditions of the dataset [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

Sahoo

Prakash

Pławiak

et al. 2022

Sensors

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Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.

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A spatiotemporal approach for vision-based hand gesture recognition using Hough transform and neural network

Cited by 22 publications

References 22 publications

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

MVHANet: multi-view hierarchical aggregation network for skeleton-based hand gesture recognition

Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

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