Segmentation method for personnel safety helmet based on super pixel features and SVM classification

Safety helmet segmentation is one of the key technologies to realize intelligent video surveillance of personnel in coal mine,which can promote the research of personnel positioning,tracking,and safety helmet wearing detection and other related technologies.For this reason,a method of mine personnel safety helmet segmentation based on super pixel feature extraction and support vector machines (SVM) classification is proposed.Firstly,the simple linear iterative clustering (SLIC) model is used to granulate the personnel image into a certain number of super pixels with similar internal pixel color features and similar spatial locations.Secondly,the color features and gray histogram texture features of super pixels in RGB,YcbCr,Lab and HSV space are extracted,and the profile feature model of safety helmet is established by analyzing the slope variation characteristics of the two-dimensional contour line of the safety helmet.Finally,the color and texture features of the positive sample super pixels of the helmet and the background negative sample super pixels are extracted from the personnel images of training set to train the SVM classifier,and the trained SVM is used to classify the super pixels of the personnel images in the testing set into the positive samples of helmet and negative samples of background.Furthermore,the fake positive samples misclassified by the SVM are identified by the helmet profile feature model and the category correction is performed for them,the under segmented sample super pixels contain both positive sample pixels and negative sample pixels are recognized,they are processed as secondary pixel classification by obtaining the difference set between the boundary mask of the positive sample region and the contour extracted by the Prewitt operator,and the helmet pixels region of the positive sample is separated to obtain the final segmentation result of the helmet.The experiments are carried out on 100 personnel images in the testing data set,and the results show that the average precision of the proposed helmet segmentation algorithm reaches 96.94%,the average recall rate reaches 95.83%,the algorithm has good applicability and robustness for personnel helmet images in different scenarios.