Real-time gesture recognition system based on Camshift algorithm and Haar-like feature
Yushan Yu, Sheng Bi, Yaoyang Mo, Weiheng Qiu · 2016
This paper propose a real-time combined method of Camshift [1] algorithm and Haar-like feature detection [2] for tracking and recognizing hand gesture in images acquired by a possibly moving camera. A Haar-like classifier is used during the initializing of the system to acquire the user's hand color. Camshift algorithm is applied with the acquired color to track the position of the hand, accompanied with a two-dimensional Kalman filter to prevent target occlusion. An offline training with Adaboost learning algorithm [3] of various hand gestures is employed to allow the classifier to recognize static hand gestures. With the information provided by Haar-like classifier, Tracking with Camshift will have more robust performance and less likely to lost targets. As the experimental result shows, the method we proposed have better performance than simply applying any of the two methods, especially in some complicate background conditions where skin-color disturbances or occlusion exist.