Purpose

This paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YC_bC_r image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region.

Design/methodology/approach

Gesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YC_bC_r image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods.

Findings

This paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments.

Originality/value

The modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

中文翻译：

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基于支持向量机的手势检测和识别系统在不同数据集上使用距离变换的自动驾驶汽车移动应用性能评估

目的

本文旨在提出一种使用支持​​向量机 (SVM) 分类方法对手势进行分类的新方法。最初，红绿蓝手势图像在预处理阶段被转换为YC _b C _r图像，然后通过阈值处理对手掌区域进行分割。然后，将距离变换方法应用于手掌的手指分割图像。进一步地，检测手掌区域的中心点（质心），并基于检测到的手掌区域的质心使用SVM分类算法检测指尖。

设计/方法/方法

手势是身体传达信息的一种物理指示。尽管任何身体运动都可以被认为是一种姿势，但通常它起源于手或脸的运动或两者的结合。组合手势非常复杂，机器难以分类。本文提出了一种使用 SVM 分类方法对手势进行分类的新方法。最初，彩色手势图像被转换为​​ YC _b C _r图像在预处理阶段，然后通过阈值处理对手掌区域进行分割。然后，将距离变换方法应用于手掌的手指分割图像。进一步地，检测手掌区域的中心点，并使用SVM分类算法检测指尖。所提出的手势图像分类系统在“Jochen Triesch”、“Sebastien Marcel”和“11Khands”数据集手势图像上应用和测试，以评估所提出系统的效率。从灵敏度、特异性、准确性和识别率方面分析了所提出系统的性能。将所提方法在这些不同数据集上的仿真结果与传统方法进行了比较。

发现

本文提出了一种使用 SVM 分类方法对手势进行分类的新方法。距离变换法用于检测分割后的手掌区域的中心点。所提出的手势检测方法在“Jochen Triesch”数据集上实现了 96.5% 的灵敏度、97.1% 的特异性、96.9% 的准确率和 99.3% 的识别率。所提出的手势检测方法在“Sebastien Marcel”数据集上实现了 94.6% 的灵敏度、95.4% 的特异性、95.3% 的准确率和 97.8% 的识别率。所提出的手势检测方法在“11Khands”数据集上实现了 97% 的灵敏度、98% 的特异性、98.1% 的准确率和 98.8% 的识别率。所提出的手势检测方法在“Jochen Triesch”数据集图像上的识别时间为 0.52 秒，为 0。“Sebastien Marcel”数据集图像的识别时间为 71 秒，“11Khands”数据集图像的识别时间为 0.22 秒。很明显，与其他数据集图像相比，所提出的手势检测方法在“11Khands”数据集上消耗的识别率较低。因此，该数据集非常适合具有多背景环境的实时手势应用。

原创性/价值

现代世界需要更多数量的自动化系统来有效地改善我们的日常活动。当今的技术出现了触摸屏方法，用于操作或运行具有或不具有有线连接的许多设备或机器。这也对自动驾驶汽车产生了影响，自动驾驶汽车可以在不与驾驶员进行任何交互的情况下进行操作。这可以通过手势识别系统实现。该手势识别系统将实时手势（人手的物理运动）捕获为数字图像，并使用预先存储的手势集对其进行识别。