publication . Other literature type . Article . Conference object . Preprint . 2018

Crowd Counting via Scale-Adaptive Convolutional Neural Network

Lu Zhang; Miaojing Shi; Qiaobo Chen;
Open Access
  • Published: 12 Mar 2018
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Comment: IEEE Winter Conf. on Applications of Computer Vision (WACV'18)
Persistent Identifiers
Subjects
free text keywords: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Computer Science - Computer Vision and Pattern Recognition, Task analysis, Pattern recognition, Computer science, Pedestrian, Receptive field, Feature extraction, Crowd counting, Image resolution, Convolutional neural network, Architecture, Artificial intelligence, business.industry, business
35 references, page 1 of 3

[1] L. Boominathan, S. S. Kruthiventi, and R. V. Babu. Crowdnet: a deep convolutional network for dense crowd counting. In ACM MM, 2016. 1, 2, 3, 4, 5, 6, 7

[2] G. J. Brostow and R. Cipolla. Unsupervised bayesian detection of independent motion in crowds. In CVPR, 2006. 2 [OpenAIRE]

[3] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In CVPR, 2008. 1, 2

[4] A. B. Chan and N. Vasconcelos. Bayesian poisson regression for crowd counting. In ICCV, 2009. 1, 2

[5] K. Chen, C. C. Loy, S. Gong, and T. Xiang. Feature mining for localised crowd counting. In BMVC, 2012. 2

[6] D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In CVPR, 2012. 2

[7] W. Ge and R. T. Collins. Marked point processes for crowd counting. In CVPR, 2009. 1

[8] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.

[9] H. Idrees, I. Saleemi, C. Seibert, and M. Shah. Multi-source multi-scale counting in extremely dense crowd images. In CVPR, 2013. 1, 2, 4, 6, 7

[10] H. Idrees, K. Soomro, and M. Shah. Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning. TPAMI, 37(10):1986-1998, 2015. 1

[11] D. Kong, D. Gray, and H. Tao. Counting pedestrians in crowds using viewpoint invariant training. In BMVC, 2005. 2

[12] V. Lempitsky and A. Zisserman. Learning to count objects in images. In NIPS, 2010. 2, 6, 7 [OpenAIRE]

[13] M. Li, Z. Zhang, K. Huang, and T. Tan. Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In ICPR, 2008. 1

[14] T.-Y. Lin, P. Dolla´r, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. 2017. 2

[15] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dolla´r, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV. 7

35 references, page 1 of 3
Abstract
Comment: IEEE Winter Conf. on Applications of Computer Vision (WACV'18)
Persistent Identifiers
Subjects
free text keywords: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Computer Science - Computer Vision and Pattern Recognition, Task analysis, Pattern recognition, Computer science, Pedestrian, Receptive field, Feature extraction, Crowd counting, Image resolution, Convolutional neural network, Architecture, Artificial intelligence, business.industry, business
35 references, page 1 of 3

[1] L. Boominathan, S. S. Kruthiventi, and R. V. Babu. Crowdnet: a deep convolutional network for dense crowd counting. In ACM MM, 2016. 1, 2, 3, 4, 5, 6, 7

[2] G. J. Brostow and R. Cipolla. Unsupervised bayesian detection of independent motion in crowds. In CVPR, 2006. 2 [OpenAIRE]

[3] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In CVPR, 2008. 1, 2

[4] A. B. Chan and N. Vasconcelos. Bayesian poisson regression for crowd counting. In ICCV, 2009. 1, 2

[5] K. Chen, C. C. Loy, S. Gong, and T. Xiang. Feature mining for localised crowd counting. In BMVC, 2012. 2

[6] D. Ciregan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In CVPR, 2012. 2

[7] W. Ge and R. T. Collins. Marked point processes for crowd counting. In CVPR, 2009. 1

[8] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.

[9] H. Idrees, I. Saleemi, C. Seibert, and M. Shah. Multi-source multi-scale counting in extremely dense crowd images. In CVPR, 2013. 1, 2, 4, 6, 7

[10] H. Idrees, K. Soomro, and M. Shah. Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning. TPAMI, 37(10):1986-1998, 2015. 1

[11] D. Kong, D. Gray, and H. Tao. Counting pedestrians in crowds using viewpoint invariant training. In BMVC, 2005. 2

[12] V. Lempitsky and A. Zisserman. Learning to count objects in images. In NIPS, 2010. 2, 6, 7 [OpenAIRE]

[13] M. Li, Z. Zhang, K. Huang, and T. Tan. Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In ICPR, 2008. 1

[14] T.-Y. Lin, P. Dolla´r, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. 2017. 2

[15] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dolla´r, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV. 7

35 references, page 1 of 3
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