A Diving Posture Recognition Method Based on Multiple Features Fusion

摘要

This paper presented an effective method to recognize diving posture in diving competition videos, which was composed of object segmentation and feature extraction. In the first stage, Lucas_kanade optical flow method is used to estimate the global motion and the object area. Then we use the skin color distribution characteristics in YCbCr space to detect accurately the athletes' skin color. Next, projection method is used to eliminate noise and segment object. In extracting features stage, we extract color, aspect ratio, area proportion and SIFT features. These features are extracted to recognize every kind of diving posture by support vector machine. The experimental results show that this method for recognizing diving posture has good recognition performance and robustness.