Abstract A real-time robust human detection and tracking system is proposed, which can detect people in the monitoring area and then keep tracking. To reduce the working range, a background subtraction technique is used to segment the moving foreground and the background. Since each body feature has its optimum working distance, several different detectors such as frontal face, head, and pedestrian are combined. By taking the video sequence continuity and the human body geometry constraint into account, robust real-time detection is achieved. The proposed system reduces the occurrence of tracking failure and enhances performance even with dramatic distance change between camera and people.
[1] Zhang C, Zhang Z Y. A Survey of Recent Advances in Face Detection. Technical Report, MSR-TR-2010-66. Redmond, USA: Microsoft Research, 2010 [2] Castrillón M, Déniz O, Guerra C, et al. ENCARA2: Real-Time Detection of Multiple Faces at Different Resolutions in Video Streams. Journal of Visual Communication and Image Representation, 2007, 18(2): 130-140 [3] Dollar P, Wojek C, Schiele B, et al. Pedestrian Detection: A Benchmark // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 304-311 [4] Amjad A, Griffiths A, Patwary M N. Multiple Face Detection Algorithm Using Colour Skin Modelling. IET Image Processing, 2012, 6(8): 1093-1101 [5] Zhou J P, Hoang J. Real Time Robust Human Detection and Trac- king System[EB/OL]. [2013-04-05]. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565316 [6] Stauffer C, Grimson W E L. Adaptive Background Mixture Models for Real-Time Tracking // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999. DOI: 10.1109/CVPR.1999.784637 [7] Elgammal A M, Harwood D, Davis L S. Non-parametric Model for Background Subtraction // Proc of the 6th European Conference on Computer Vision. Dublin, Ireland, 2000, Ⅱ: 751-767 [8] Kim K, Chalidabhongse T H, Harwood D, et al. Real-Time Foreground-Background Segmentation Using Codebook Model. Real-Time Imaging, 2005, 11(3): 172-185 [9] Viola P, Jones M. Rapid Object Detection Using a Boosted Cascade of Simple Features // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA, 2001, I: 511-518 [10] Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, I: 886-893 [11] Lienhart R, Maydt J. An Extended Set of Haar-Like Features for Rapid Object Detection // Proc of the International Conference on Image Processing. New York, USA, 2002, I: 900-903 [12] Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422
2014, Vol. 27 
