An automatic people counting method of hotel dining with occlusion
DOI: 10.23977/jaip.2016.11001 | Downloads: 62 | Views: 2528
Dong Ling 1, Chen Xianqiao 1
1 College of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430063, China
Corresponding AuthorDong Ling
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.
KEYWORDSpeople counting; scenes with occlusion; Adab_SVM algorithm; head detection; eigenvector
CITE THIS PAPER
Ling, D. and Xianqiao, C. (2016) A automatic people counting method of hotel dining with occlusion. Journal of Artificial Intelligence Practice (2016) 1: 1-7.
 Nguyen D, Huynh L, Dinh T B, et al. Video Monitoring System: Counting People by Tracking[C]// IEEE Rivf International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future. IEEE, 2012:1-4.
 Zebin Cai, Yu Zhu Liang, Hao Liu, et al. Counting people in crowded scenes by video analyzing[C]// IEEE, Conference on Industrial Electronics and Applications. 2014:1841-1845.
 Tongyi Sun. Research on People Counting in Real-time Video Surveillance[D]. National University of Defense Technology, 2011.(in Chinese)
 Wen W, Ho M, Huang C. People tracking and counting for applications in video surveil-lance system[C]// Audio, Language and Image Processing, 2008. ICALIP 2008. Interna-tional Conference on. IEEE, 2008:1677 - 1682.
 Bingkun Zhang. The research of algorithm of people counting base on the head detec-tion[D]. Xi'An University of Science and Technology, 2013.(in Chinese)
 Ying Cao, Qiguang Miao, Jiachen Liu, et al. ACTA AUTOMATICA SINICA, 2013, 39(6):745-758.(in Chinese)
 Jianming Cui, Jianming Liu, Zhouyu Liao. Research of Text Categorization Based on Support Vector Machine[J]. Computer Simulation, 2013, 30(2):299-302.(in Chinese)
 Wennuan Ou, Xuhong Tian, Tonglin Zhu. JOURNAL OF GRAPHICS, 2012, 33(2):113-118.(in Chinese)
 Chen C C, Lin H H, Chen T C. Tracking and counting people in visual surveillance sys-tems[C]// International Conference on Acoustics. 2011:1425-1428.
 Kuo C H, Nevatia R. How does person identity recognition help multi-person track-ing?[C]// IEEE Conference on Computer Vision & Pattern Recognition. 2011:1217-1224.
 Hong Liu, Ru Song, Baoxing Wang. Journal of Anhui University(Natural Science Edition), 2015(3):47-50.(in Chinese)