计算机技术﹑电信技术 |
|
|
|
|
基于正则化风险最小化的目标计数 |
吴鹏洲,于慧敏,曾雄 |
浙江大学 信息与电子工程学系,浙江 杭州 310027 |
|
Object counting based on regularized risk minimization |
WU Peng-zhou, YU Hui-min, ZENG Xiong |
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China |
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. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|