Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

Dardas, Nasser; Georganas, N.D.

doi:10.1109/tim.2011.2161140

Cited by 434 publications

(184 citation statements)

References 41 publications

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“…In order to overcome these shortcomings and improve the robustness of the gesture recognition, a static gesture recognition algorithm is proposed based on upper triangular image texture and recursive graph (UTTRG-SGR). The experimental results show that the proposed method is more accurate than the algorithm in reference [10] by 6.17% and than the algorithm in reference [11] by 5.02%. Also, the running time is reduced by 38.7% and 47.8%.…”

Section: Introductionmentioning

confidence: 92%

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Cai

2017

J. Eng. Technol. Sci.

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Abstract.A static gesture recognition algorithm is proposed based on a recursive graph of the upper triangular image texture, motivated by the low accuracy and robustness of existing algorithms. Firstly, the fingertip localization method based on contour curvature is used to obtain the palm region and then the gesture contour model is established. Secondly, a recurrence plot of the gesture contour sequence is built, which is constructed using the central point and the starting point coordinates. Finally, the texture recognition algorithm is applied to calculate the normalized distance between the recurrence plots of the gesture. The experimental results show that the proposed algorithm can achieve higher recognition accuracy under varying complex backgrounds and illumination. At the same time, when the gesture is in rotation, translation, or scaling, the algorithm has high robustness with a small amount of computation and high efficiency.

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Section: Introductionmentioning

confidence: 92%

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Cai

2017

J. Eng. Technol. Sci.

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“…The output takes discrete values in classification and continuous values in regression. Spatial gestures form a static classification problem for which algorithms, such as naïve Bayes [12], [32], [33], k-nearest neighbor (k-NN) [34], [35], adaptive boosting (AdaBoost) [29], [36], support vector machines (SVM) [14], [37], and decision trees [38], have been used. On the other hand, a temporal classification problem wherein real-time tracking is performed requires different algorithms, such as hidden Markov models [28], [39], and dynamic time warping [30].…”

Section: Machine Learning Algorithms For Gesture Detectionmentioning

confidence: 99%

Gesture tank: a gesture detection water vessel for foot movements

Gunawardena

Hirakawa

2016

Int J on Adv. in ICT for Emerging Countries

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Abstract-Computers have become ubiquitous and integrated into our day-to-day activities. Researchers have been exploring mechanisms for interacting with computers using natural means in natural environments. Water interaction is a perfect example. This paper presents our attempt to use foot gestures performed in water as an interaction mechanism. It is an extension of our previous study for detecting objects in a water vessel. An experiment was performed to determine which foot-based gestures are suitable for implementation, and we proceeded to recognize a selected set of gestures using machine-learning techniques. We present our findings regarding which algorithms provide the best recognition rates.

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“…Emerging research that moves beyond typical binary models of positive/negative class association for open set recognition has examined the issues of detecting novel classes [16,5,4], rejecting outlier or unknown classes [24,57,2], and/or simultaneously detecting and recognizing known classes in the midst of unknown classes [45,11,14]. These approaches have been a good start, but they do not directly address the overarching problem: multi-class open set recognition, wherein models should account for multiple known classes as well as provide an option to detect novel classes or reject unknown…”

Section: Introductionmentioning

confidence: 99%

Multi-class Open Set Recognition Using Probability of Inclusion

Jain

Scheirer

Boult

2014

Lecture Notes in Computer Science

277

148

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Abstract. The perceived success of recent visual recognition approaches has largely been derived from their performance on classification tasks, where all possible classes are known at training time. But what about open set problems, where unknown classes appear at test time? Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under an assumption of incomplete class knowledge. In this paper, we formulate the problem as one of modeling positive training data at the decision boundary, where we can invoke the statistical extreme value theory. A new algorithm called the PI -SVM is introduced for estimating the unnormalized posterior probability of class inclusion.

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Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

Cited by 434 publications

References 41 publications

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Gesture tank: a gesture detection water vessel for foot movements

Multi-class Open Set Recognition Using Probability of Inclusion

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