Fingertip Tracking Based on Edge Feature and Pixel Ratio in Gesture

Abstract

Fingertip recognition and tracking is a key problem in gesture recognition. The current fingertip locating and tracking method is complicated, or needs to be labelled artificially. In this paper, a particle filtering method based on edge feature and pixel ratio is proposed to track the target finger in a complex background. We set the region of each particle as a fixed value, and calculate the edge orientation histograms and the proportion of the body pixels of each particle area. The similarity degree of edge features is measured by the Bhattacharyya distance, and a new similarity measure is defined to measure the similarity degree of pixel ratio. These two feature similarities will be linearly combined to track the target model. And then we calculate the farthest point from the center of the contour in the predicted model and update the target model again. The results show that the method can track the target fingertip accurately and effectively in real time, under the condition of interference.