CrowdNet: A deep convolutional network for dense crowd counting

MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Volumn , Issue , 2016, Pages 640-644

  Boominathan, Lokesh ^a   Kruthiventi, Srinivas S S ^a   Venkatesh Babu, R ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Convolutional neural networks; Crowd density

Indexed keywords

NEURAL NETWORKS; SAMPLING; SEMANTICS;

CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; CROWD DENSITY; HIGH LEVEL SEMANTICS; LOW-LEVEL FEATURES; MULTI-SCALE DATUM; SCALE INVARIANT; STATE-OF-THE-ART METHODS;

CONVOLUTION;

EID: 84994613610     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2964284.2967300     Document Type: Conference Paper

References (19)

1. G. J. Brostow and R. Cipolla. Unsupervised bayesian detection of independent motion in crowds. In IEEE Conference on Computer Vision and Pattern Recognition, 2006. 2
2. A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In IEEE Conference on Computer Vision and Pattern Recognition, 2008. 2
3. K. Chen, C. C. Loy, S. Gong, and T. Xiang. Feature mining for localised crowd counting. In BMVC, 2012. 2
4. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014. 3.1.1
5. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In ICLR, 2015. 3.1.1, 4
6. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005., volume 1, pages 886-893. IEEE, 2005. 1
7. J. Ferryman and A. Ellis. Pets2010: Dataset and challenge. In IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2010. 2
8. H. Idrees, I. Saleemi, C. Seibert, and M. Shah. Multi-source multi-scale counting in extremely dense crowd images. In IEEE Conference on Computer Vision and Pattern Recognition, 2013. 1, 2, 4, 4.1
9. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014. 4
10. D. Kong, D. Gray, and H. Tao. A viewpoint invariant approach for crowd counting. In International Conference on Pattern Recognition, 2006. 2
11. S. S. Kruthiventi, K. Ayush, and R. V. Babu. Deepfix: A fully convolutional neural network for predicting human eye fixations. arXiv preprint arXiv:1510.02927, 2015. 3.1.1
12. V. Lempitsky and A. Zisserman. Learning to count objects in images. In Advances in Neural Information Processing Systems, 2010. 4.1
13. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1
14. G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille. Weakly-and semi-supervised learning of a dcnn for semantic image segmentation. arxiv:1502.02734, 2015. 4
15. V. Rabaud and S. Belongie. Counting crowded moving objects. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006. 2
16. M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert. Density-aware person detection and tracking in crowds. In IEEE International Conference on Computer Vision, 2011. 4.1
17. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 3.1.1
18. C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao. Deep people counting in extremely dense crowds. In Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, 2015. 2, 3.2
19. C. Zhang, H. Li, X. Wang, and X. Yang. Cross-scene crowd counting via deep convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition, 2015. 2, 3.2, 4, 4.1