A real-time hand detection system based on multi-feature

This paper describes a real-time hand detection system which can reach high speed and accuracy. The system is based on Gentle Adaboost and cascade classifier. To improve the performance of the system, three efficient features are selected to describe the visual properties of human hands. In addition, the detection is accelerated due to several optimization methods, including the method for fast calculation of HOG features, improved cascade classifier and skin-color pre-detection. Experiments were performed on our self-constructed dataset, the results showed that the detection rate of the system can reach 0.889 while the false rate is 0.010 at the speed of 32.6339 ms per frame on a Intel Core i5-2400 CPU running at 3.1 GHz.