Crowd Counting with Deep Negative Correlation Learning

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2018, Pages 5382-5390

  Shi, Zenglin ^a   Zhang, Le ^b   Liu, Yun ^c   Cao, Xiaofeng ^d   Ye, Yangdong ^e   Cheng, Ming Ming ^c   Zheng, Guoyan ^a  

^a UNIVERSITY OF BERN   (Switzerland)

^b ADSC   (Singapore)

^c NANKAI UNIVERSITY   (China)

^d UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^e ZHENGZHOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; NETWORK ARCHITECTURE;

CONVOLUTIONAL NETWORKS; GENERALIZATION CAPABILITY; INTRINSIC DIVERSITY; LEARNING STRATEGY; NEGATIVE CORRELATION LEARNING; NETWORK STRUCTURES; SINGLE IMAGES; STATE-OF-THE-ART METHODS;

DEEP LEARNING;

EID: 85057568187     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00564     Document Type: Conference Paper

References (33)

1. V. Lempitsky and A. Zisserman, "Learning to count objects in images, " in Advances in Neural Information Processing Systems, 2010, pp. 1324-1332.
2. A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos, "Privacy preserving crowd monitoring: Counting people without people models or tracking, " in roceedings of the IEEE conference on Computer Vision and Pattern Recognition. IEEE, 2008, pp. 1-7.
3. A. B. Chan and N. Vasconcelos, "Counting people with lowlevel features and Bayesian regression, " IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2160-2177, 2012.
4. Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, "Singleimage crowd counting via multi-column convolutional neural network, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 589-597.
5. C. Zhang, H. Li, X.Wang, and X. Yang, "Cross-scene crowd counting via deep convolutional neural networks, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 833-841.
6. T. Zhao, R. Nevatia, and B. Wu, "Segmentation and tracking of multiple humans in crowded environments, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1198-1211, 2008.
7. D. Ryan, S. Denman, S. Sridharan, and C. Fookes, "An evaluation of crowd counting methods, features and regression models, " Computer Vision and Image Understanding, vol. 130, pp. 1-17, 2015.
8. Z. Shi, Y. Ye, and Y.Wu, "Rank-based pooling for deep convolutional neural networks, " Neural Networks, vol. 83, pp. 21-31, 2016.
9. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-Time object detection with region proposal networks, " in Advances in Neural Information Processing Systems, 2015, pp. 91-99.
10. F. Schroff, D. Kalenichenko, and J. Philbin, "Facenet: A unified embedding for face recognition and clustering, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 815-823.
11. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, " arXiv preprint arXiv: 1606.00915, 2016.
12. H. Nam and B. Han, "Learning multi-domain convolutional neural networks for visual tracking, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4293-4302.
13. C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao, "Deep people counting in extremely dense crowds, " in Proceedings of the 23rd ACM international conference on Multimedia. ACM, 2015, pp. 1299-1302.
14. L. Boominathan, S. S. Kruthiventi, and R. V. Babu, "Crowdnet: A deep convolutional network for dense crowd counting, " in Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016, pp. 640-644.
15. D. Onoro-Rubio and R. J. López-Sastre, "Towards perspective-free object counting with deep learning, " in European Conference on Computer Vision. Springer, 2016, pp. 615-629.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks, " in Advances in Neural Information Processing Systems, 2012, pp. 1097-1105.
17. D. B. Sam, S. Surya, and R. V. Babu, "Switching convolutional neural network for crowd counting, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, no. 3, 2017, p. 6.
18. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " CoRR, vol. Abs/1409.1556, 2015.
19. Y. Liu and X. Yao, "Ensemble learning via negative correlation, " Neural Networks, vol. 12, no. 10, pp. 1399-1404, 1999.
20. G. Brown, J. L. Wyatt, and P. Ti?no, "Managing diversity in regression ensembles, " Journal of Machine Learning Research, vol. 6, no. Sep, pp. 1621-1650, 2005.
21. Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble classification and regression-recent developments, applications and future directions, " IEEE Computational Intelligence Magazine, vol. 11, no. 1, pp. 41-53, 2016.
22. M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, "Do we need hundreds of classifiers to solve real world classification problems, " Journal of Machine Learning Research, vol. 15, no. 1, pp. 3133-3181, 2014.
23. L. Zhang and P. N. Suganthan, "Benchmarking ensemble classifiers with novel co-Trained kernal ridge regression and random vector functional link ensembles [research frontier], " IEEE Computational Intelligence Magazine, vol. 12, no. 4, pp. 61-72, 2017.
24. L. Breiman, "Random forests, " Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
25. Y. Liu, X. Yao, and T. Higuchi, "Evolutionary ensembles with negative correlation learning, " IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 380-387, 2000.
26. F. Yu and V. Koltun, "Multi-scale context aggregation by dilated convolutions, " arXiv preprint arXiv: 1511.07122, 2015.
27. L. Wang, W. Ouyang, X. Wang, and H. Lu, "Stct: Sequentially training convolutional networks for visual tracking, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1373-1381.
28. M. Marsden, K. McGuiness, S. Little, and N. E. O'Connor, "Fully convolutional crowd counting on highly congested scenes, " arXiv preprint arXiv: 1612.00220, 2016.
29. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding, " CoRR, vol. Abs/1408.5093, 2014.
30. H. Idrees, I. Saleemi, C. Seibert, and M. Shah, "Multi-source multi-scale counting in extremely dense crowd images, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2547-2554.
31. M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert, "Density-Aware person detection and tracking in crowds, " in Proceedings of the IEEE International Conference on Computer Vision. IEEE, 2011, pp. 2423-2430.
32. L. Zeng, X. Xu, B. Cai, S. Qiu, and T. Zhang, "Multi-scale convolutional neural networks for crowd counting, " arXiv preprint arXiv: 1702.02359, 2017.
33. J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, "Rotation forest: A new classifier ensemble method, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1619-1630, 2006.