Vision-based Hand Gesture Interaction Using Particle Filter, Principle Component Analysis and Transition Network

Abstract

Vision-based human-computer interaction is becoming important nowadays. It offers natural interaction with computers and frees users from mechanical interaction devices, which is favourable especially for wearable computers. This paper presents a human-computer interaction system based on a conventional webcam and hand gesture recognition. This interaction system works in real time and enables users to control a computer cursor with hand motions and gestures instead of a mouse. Five hand gestures are designed on behalf of five mouse operations: moving, left click, left-double click, right click and no-action. An algorithm based on Particle Filter is used for tracking the hand position. PCA-based feature selection is used for recognizing the hand gestures. A transition network is also employed for improving the accuracy and reliability of the interaction system. This interaction system shows good performance in the recognition and interaction test.

Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

“…Although this method has produced good results in terms of hand region detection since the detection rate can be as high as 98.75%, it is yet to be further improved to detect the hand shape where the accuracy of shape detection reaches 88.02%. Wang et al (2014) detected skin colour by utilising HSV colour space (Hue, Saturation, and Value). A binary image was obtained according to the HSV colour range of hand skin.…”

Hand Gesture Trajectory Estimation Using Keypoints Combination of Brisk and Minimum Eigenvalue Techniques for Human Computer Interaction Applications

“…Motion cue has been used in a few approaches for hand gesture segmentation and detection because it is sensitive to shadow, blurring and many moving objects in the background [1,16,17]. However, motion cue can be efficiently applied to hand detection and segmentation by makings it work in association with other extracted features such as skin color and orientation features [11,18].…”