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Algorithm of local features fusion and modified covariance-matrix technique for hand motion position estimation and hand gesture trajectory tracking approach

机译：局部特征融合算法融合与修改协方差 - 矩阵技术，用于手动位置估计和手势轨迹跟踪方法

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Nowadays, visual recognition based dynamic hand gesture tracking has gained very considerable attention. Hand gestures can play an important role as a non-touchable communication tool between machines and humans based on using the affordable built-in webcam. The need for replacing touch-based computing devices interaction has been increasing in many fields like healthcare, security, and generally as interface-based devices controlling. Especially with COVID19 outbreak spreading around the world, where people take a risk and avoid dealing with electronic consumer machines that required hand touching. However, in any dynamic hand gesture recognition system, dynamic hand gesture tracking is a very hard task, where position estimation over video frames for freely moving hand in the air is quite challenging. This shortcoming is due to hand great scale changes, posture variations, and translation problems. Hence, to tackle these difficulties and extract gesture features for gesture recognition phase accurately, this paper proposed an algorithm of dynamic hand trajectory tracking for gesture recognition. The presented algorithm proposes local features fusion based on Gabor-Canny- Hog features embedded an updated compact covariance matrix technique as sophisticated feature-based tracking, utilizing video sequences of IBGHT dataset. As a result, the proposed approach has shown adorable optimization achieving an accuracy rate of 96.97%, overcoming the problems of hand appearance variation in the complicated environment.
机译：如今，基于视觉识别的动态手势跟踪已经获得了非常相当大的关注。手势可以在基于使用经济实惠的内置网络摄像头的机器和人类之间是一种重要的作用。替换基于触摸的计算设备交互的需求在许多领域都在增加，如医疗保健，安全性，并且通常是基于接口的设备控制。特别是在世界各地的Covid19爆发，人们承担风险，避免处理需要手感的电子消费机器。然而，在任何动态手势识别系统中，动态手势跟踪是一个非常硬的任务，其中在空中自由移动的视频框架上的位置估计是非常具有挑战性的。这种缺点是由于手掌变化，姿势变化和翻译问题。因此，为了准确地解决这些困难并提取手势识别相位的手势特征，提出了一种用于手势识别的动态手动轨迹跟踪算法。呈现的算法提出了基于Gabor-Canny-Hog特征的本地特征融合，其更新了更新的紧凑协方差矩阵技术，作为基于复杂的基于功能的跟踪，利用IBGHT数据集的视频序列。因此，该方法表明了可爱的优化，实现了96.97％的准确率，克服了复杂环境中的手外观变化问题。

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