An automatic people counting method of hotel dining with occlusion

Video image has the advantage of large amount of information, good real-time performance and low cost, so automatic people counting based on video image has very high practical value, and many scholars have done a large number of experiments and studies on this and achieved certain achievements. But for scenes with more occlusion and background changing quickly and without obvious rules, it’s difficult to count accurately. In order to improve the counting accuracy in the above scenes, to provide the number of customers for hotel managers to efficiently organize and work, based on pictures, a automatic people counting method using SVM as weak classifiers, train intensively in learning by Adaboost algorithm(i.e. Adab_SVM algorithm) of hotel dining is proposed. The method is mainly aimed at the hotel scenes with occlusion too much to complete the segmentation of human body region. Firstly, traversing the entire picture to get the preliminary head areas and the number of people, then merge these head areas to get the exact number of people, to complete the statistical work on the number of people of the entire picture. Experimental results show that the method has higher counting accuracy in the hotel scenes with occlusion.