Dynamic hand gesture recognition using combination of two-level tracker and trajectory-guided features

Saboo, Shweta; Singha, Joyeeta; Laskar, Rabul Hussain

doi:10.1007/s00530-021-00811-8

Cited by 16 publications

(13 citation statements)

References 20 publications

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“…The comparison of the SDT tracking approach with the state of art models is shown in Table 5. Researchers (Misra & Laskar, 2019b; Saboo et al, 2021; Saboo & Singha, 2021) applied various shallow architecture models with KLT for bare hand detection and tracking on the NITS hand databases IV. While applying these models on the NITS hand gesture database VIIIE, performance is degraded, as shown in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…To recognize these gesticulated characters, gradient local auto‐correlation (GLAC) feature with neuro‐fuzzy classifier is applied. Researchers (Misra & Laskar, 2017; Saboo et al, 2021) extracted basic features such as distance, histogram, scale, mean, variance to capture the characteristics of the bare hand. However, they suggest that features used in the combination are better suited for bare hand detection.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, researchers (Misra & Laskar, 2017) used features like the HOG, LBP, histogram, scale‐invariant feature transform (SIFT), GLAC in the combination for detection. In addition to these features, researchers (Saboo et al, 2021) used analysis of variance (ANOVA) to determine the best subset of the features for training the classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers (Bhuyan et al, 2008; Misra et al, 2018; Misra & Hussain Laskar, 2019; Misra & Laskar, 2017; Misra & Laskar, 2019; Misra & Laskar, 2019b; Saboo et al, 2021; Saboo & Singha, 2021; Singha et al, 2018) have experimented with shallow machine learning models with hand‐crafted features to recognize the limited number of hand gestures. At most, researchers (Farahi & Yazdi, 2020) have experimented on recognizing 58 gesticulated characters to avoid the variation in pattern and style.…”

Section: Introductionmentioning

confidence: 99%

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Design and development of a vision‐based system for detection, tracking and recognition of isolated dynamic bare hand gesticulated characters

et al. 2022

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Detection and tracking are the vital stages to form the gesture trajectory in gesture recognition. It becomes more challenging when the variations in illumination, pose, position, occlusion, scale, speed, blurring effect and complex environment are introduced. Additionally, the background feature domination effect affects the existing deep learning models. A semantic segmentation model is implemented in this work to detect the bare hand to overcome these challenges. A pre‐trained network VGG‐16 is utilized by training with the proposed NITS S‐Net database. Evaluation of the SegNet model is done on EgoHands, Oxford and OUHands databases. To track the bare hand, a SegNet‐based detection and tracking approach is proposed using Kalman filter and point‐tracker. This model achieves 97.01% accuracy (a relative improvement of ~8% from the baseline models) at 0.068 s per frame computational time on NITS hand gesture database VIIIB. The gesticulated characters, that is, alphabets, numbers, operators, special characters, are gesticulated without any constraints on the pattern/strokes. To recognize these 95 multi‐stroke gestures, a deep convolutional neural network (DCNN) is presented using AlexNet. The DCNN model achieves 97.60% (a relative improvement of ~14% from the baseline models) accuracy on the NITS hand gesture database VIIIB merged. Evaluation of the handwritten EMNIST merged (balanced) database resulted in average recognition accuracy of 91.60%.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and development of a vision‐based system for detection, tracking and recognition of isolated dynamic bare hand gesticulated characters

et al. 2022

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“…The suggested method recognizes the hand using information on skin color and mobility. Hand tracking is performed using a two-level tracking system and a modified Kanade–Lucas–Tomasi (KLT) tracking algorithm [ 55 ]. Table 1 presents the comparison of the most recent selected gesture recognition studies.…”

Section: Significant Research Work On Hand Gesture Recognitionmentioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

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Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

Kirupakaran,

Laskar

2024

Machine Vision and Applications

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Dynamic hand gesture recognition using combination of two-level tracker and trajectory-guided features

Cited by 16 publications

References 20 publications

Design and development of a vision‐based system for detection, tracking and recognition of isolated dynamic bare hand gesticulated characters

Design and development of a vision‐based system for detection, tracking and recognition of isolated dynamic bare hand gesticulated characters

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

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