No abstract

In this paper, we developed a mathematical model for finger gesture identification using two colored glove. The glove is designed in such a manner that wristband and middle finger of the glove are marked by blue color and other fingers are marked by red color. HSV values of those colors are implicated in order to identify red and blue colors. After detecting colors, two processes are employed for identification of fingers. One of them is the angle created at the wristband center between the middle finger and any other fingers. Other is examining the ratio between the wristband-middle finger distance and the projection of the wristband and other fingers distance on wrist-middle finger joining line. For both processes, the middle finger must present in order to identify the fingers. After identification of fingers gesture using both methods, an application of finger detection is presented here by changing a PowerPoint slide. This mathematical model was tested on several conditions and got the accuracy of more than 82%.

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Siléane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Siléane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.