HUMAN POSTURE RECOGNITION BASED ON POINT AFFINITY FIELD AND SVM
Aimed at the problem that traditional posture recognition still needs physical data acquisition equipment or depth somatosensory equipment to manually extract features,a human posture recognition method based on point affinity field and support vector machine is proposed.The point affinity field was taken as the core to conduct the joint detection,and the coordinate information of 18 joints of various postures was obtained.The standardized coordinate data was used to train the SVM model,and different Gaussian kernel functions were selected for comparison.In the absence of human depth information and no dressing equipment,only normal RGB images were used to categorize human postures.The experiments show that it has good recognition effect in KTH and Weizmann data sets.In the self-acquisition data set,the accuracy is improved by 7 percentage points compared with the method with sensors while the operation steps are reduced.In addition,random forest and KNN algorithm are used for posture classification comparison while keeping the detection of the joints unchanged.Experimental results show that this method is superior to the latter two.
国家科技期刊平台
NSTL
万方数据
维普
