Real-Time Upper-Limbs Posture Recognition Based on Particle Filters and AdaBoost Algorithms
Chin‐Shyurng Fahn, Sheng-Lung Chiang · 2010
In this paper, we employ particle filters to dynamically locate a face and upper-limbs. To prevent from the disturbance caused by skin color regions, such as other naked parts of a human body, or some skin color-like objects in the background, we further take the motion cue as a feature during the tracking. Currently, we prescribe eight kinds of upper-limbs postures with reference to the characteristic of flag semaphore. The advantage is that we can utilize the relative positions of a face and two hands to recognize the postures easily. To achieve posture recognition, we evaluate three different classifiers using the machine learning methods: multi-layer perceptrons, support vector machines, and AdaBoost algorithms. The experimental results reveal that AdaBoost algorithms are the best one, which reach the accuracy rate of recognizing upper-limbs postures more than 95% and require much less training time than the other two do.