Fingertip Detection Algorithm And Application Based On Single Camera

With the rapid development of computer vision, the human-computer interaction technology based on computer vision is accordingly progress. By using the traditional mouse, keyboard and other physical input media, human computer interaction mode has not been able to meet the needs of people. So the faster and more convenient way of human-computer interaction become mainstream topics of current research, using computer vision to extract human action, and using program to calculate then the computer will gives the corresponding feedback action, which is the ultimate goal of human-computer interaction.Among them, communicating with computer by hand is the most convenient and feasible method of that human-computer interaction. So human-computer interaction based on computer vision has been proposed. It is used in somatosensory games, smart home and some other. The fingertip detection is the key of finger interaction system, this paper uses the fingertip position to control the movement of the mouse. Fingertip detection research has two directions: two-dimensional and three-dimensional space. Of course, in three-dimensional space there are many data types detecting fingertip, so the recognition rate will be higher in this space. But at the same time, it’s rich data may take out consume more time, and practicability and convenience are poor, practicality and convenience are poor, so this paper focuses on the detection of fingertip under two-dimensional monocular vision. Fingertip detection can be divided into two kinds of dynamic and static, dynamic detection should be carried out as a background segmentation in which hand foreground is extracted. Because the mean of background subtraction method is more real-time, and a good result of segmentation under fixed lens camera, so we choose it. And then we use skin color as a judgment of segmentation, this paper through two ways to determine skin color model, the comparison experiment and the skin color clustering. Then we choose YCb Cr skin color model for the image with threshold skin pixels are extracted, but because the threshold cannot be adaptive, so there are still segmentation effect is not good. Therefore, we use the circular gradient algorithm, which can set seed in the shape of hand segmentation and fill the holes of skin color detection, and get a better completed hand segmentation. After morphological processing, we use the Canny operator to extract the rounded hand contour, which prepare for the next fingertip accurate detection. Then, we use the improved algorithm to extract the K value of the fingertip detection algorithm to detect the fingertip position accurately. We do many experiments to prove effectiveness of the proposed algorithm in the paper. Finally, using the detected fingertip position and the mouse information to control the mouse, and achieve a simple application of the algorithm.