1］ LIN S F, CHEN J Y, CHAO H X. Estimation of number of people in crowded scenes using perspective transformation ［J］. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans,  2001, 31(6): 645-654．
［2］ DESCOMBES X, MINLOS R, ZHIZHINA E. Object extraction using a stochastic birth-and-death dynamics in continuum ［J］. Journal of Mathematical Imaging and Vision, 2009, 33(3): 347-359．
［3］ DAVIES A C, YIN J H, VELASTIN S A. Crowd monitoring using image processing ［J］. Electronics and Communication Engineering Journal, 1995, 7(1):37-47．
［4］ PARAGIOS N, RAMESH V. A MRF-based approach for real-time subway monitoring ［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. ［S.l.］: IEEE, 2001: I-1034-I-1040．
［5］ MA R, LI L, HUANG W, et al. On pixel count based crowd density estimation for visual surveillance ［C］∥Proceedings of IEEE Conference on Cybernetics and Intelligent System. ［S.l.］: IEEE, 2004: 170-173．
［6］ VELASTIN S A, YIN J H, DAVIES A C, et al. Automated measurement of crowd density and motion using image processing ［C］∥Proceedings of IET International Conference on Road Traffic Monitoring and Control. ［S.l.］: IET, 1994: 127-132．
［7］ REGAZZONI C S, TESEI A. Distributed data fusion for real-time crowding estimation ［J］. Signal Processing, 1996, 53(1): 47-63．
［8］ CHO S Y, CHOW T W S, LEUNG C T. A neural-based crowd estimation by hybrid global learning algorithm ［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1999, 29(4): 535-541．
［9］ MARANA A, DA COSTA L, LOTUFO R, et al. On the efficacy of texture analysis for crowd monitoring ［C］∥Proceedings of IEEE International Symposium on Computer Graphics, Image Processing, and Vision. ［S.l.］: IEEE, 1998: 354-361．
［10］ LI W, WU X, MATSUMOTO K, et al. Crowd density estimation: an improved approach ［C］∥Proceedings of IEEE International Conference on Signal Processing. ［S.l.］: IEEE, 2010: 1213-1216．
［11］ VERONA V V, MARANA A N. Wavelet packet analysis for crowd density estimation ［C］∥Proceedings of the IASTED International Symposia on Applied Informatics. Innsbruck: ［s.n.］, 2001．
［12］ RAHMALAN H, NIXON M, CARTER J. On crowd density estimation for surveillance ［C］∥Proceedings of The Institution of Engineering and Technology Conference on Crime and Security. ［S.l.］: IET, 2006: 540-545．
［13］ WU X Y, LIANG G Y, LEE K K, et al. Crowd density estimation using texture analysis and learning ［C］∥ Proceedings of IEEE International Conference on Robotics and Biomimetics. ［S.l.］: IEEE, 2006: 214-219．
［14］ CHAN A B, LIANG Z, VASCONCELOS N. Privacy preserving crowd monitoring: counting people without people models or tracking ［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. ［S.l.］: IEEE, 2008: 17．
［15］ CHAN A B, VASCONCELOS N. Bayesian Poisson regression for crowd counting ［C］∥Proceedings of IEEE International Conference on Computer Vision. ［S.l.］: IEEE, 2009: 545-551．
［16］ RYAN D, DENMAN S, FOOKES C, et al. Crowd counting using multiple local features ［C］∥Proceedings of IEEE Conference on Digital Image Computing: Techniques and Applications. ［S.l.］: IEEE, 2009: 81-88．
［17］ SCHREIBER D, RAUTER M. A CPU-GPU hybrid people counting system for real-world airport scenarios using arbitrary oblique view cameras ［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. ［S.l.］: IEEE, 2012: 83-88．
［18］ MURPHY K P. Machine learning: a probabilistic perspective ［M］. ［S.l.］: MIT, 2012: 204-207．
［19］ LEMPITSKY V, ZISSERMAN A. Learning to count objects in images ［C］∥Proceedings of Neural Information Processing Systems (NIPS). Vancouver: Curran Associates Inc,2010．
［20］ BENTLEY J L. Programming pearls: perspective on performance ［J］. Communications of the ACM, 1984, 27(11): 1087-1092．
［21］ BENTLEY J L. Programming pearls: algorithm design techniques ［J］. Communications of the ACM, 1984, 27(9): 865-871．
［22］ SRA S, NOWOZIN S, WRIGHT S J. Optimization for machine learning ［M］. ［S.l.］: MIT, 2012: 185-196．
［23］ LOWE D G. Distinctive image features from scale-invariant keypoints ［J］. International Journal of Computer Vision, 2004, 60(2): 91-110.