Fetching the paper…
Reading the bibliography…
Crowd localization is a new computer vision task, evolved from crowd counting.
J. Wan, Z. Liu, and A. B. Chan, “A generalized loss function for crowd counting and localization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 1974–1983
1983
Earlier work this paper cites.
X. Liu, P. H. Tu, J. Rittscher, A. Perera, and N. Krahnstoever, “Detecting and counting people in surveillance applications,” in IEEE Conference on Advanced Video and Signal Based Surveillance, 2005. IEEE, 2005, pp. 306–311
2005
Earlier work this paper cites.
P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” International journal of computer vision , vol. 61, no. 1, pp. 55–79, 2005
2005
Earlier work this paper cites.
M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in 2008 IEEE Conference on computer vision and pattern recognition . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
M. Andriluka, S. Roth, and B. Schiele, “Pictorial structures revisited: People detection and articulated pose estimation,” in 2009 IEEE conference on computer vision and pattern recognition . IEEE, 2009, pp. 1014–1021
2009
Earlier work this paper cites.
M. Rodriguez, I. Laptev, J. Sivic, and J.-Y. Audibert, “Density-aware person detection and tracking in crowds,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 2423–2430
2011
Earlier work this paper cites.
T. Van Oosterhout, S. Bakkes, B. J. Kröse et al. , “Head detection in stereo data for people counting and segmentation,” in VISAPP , 2011, pp. 620–625
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
Y. Yuan, J. Fang, and Q. Wang, “Online anomaly detection in crowd scenes via structure analysis,” IEEE transactions on cybernetics , vol. 45, no. 3, pp. 548–561, 2014
2014
Earlier work this paper cites.
A. S. Rao, J. Gubbi, S. Marusic, and M. Palaniswami, “Crowd event detection on optical flow manifolds,” IEEE transactions on cybernetics , vol. 46, no. 7, pp. 1524–1537, 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NIPS , 2015, pp. 91–99
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015, pp. 448–456
2015
Earlier work this paper cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, F. Li, and S. Savarese, “Social LSTM: human trajectory prediction in crowded spaces,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 2016, pp. 961–971. [Online]. Available: https://doi.org/10.1109/CVPR.2016.110
2016
Earlier work this paper cites.
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 779–788
2016
Earlier work this paper cites.
R. Stewart, M. Andriluka, and A. Y. Ng, “End-to-end people detection in crowded scenes,” in CVPR , 2016, pp. 2325–2333
2016
Earlier work this paper cites.
L. J. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” CoRR , vol. abs/1607.06450, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” in CVPR , 2016, pp. 589–597
2016
Earlier work this paper cites.
X. Li, M. Chen, F. Nie, and Q. Wang, “A multiview-based parameter free framework for group detection,” in AAAI , 2017
2017
Earlier work this paper cites.
P. Hu and D. Ramanan, “Finding tiny faces,” in CVPR , 2017, pp. 951–959
2017
Earlier work this paper cites.
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 936–944
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in CVPR , 2017, pp. 2117–2125
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Liu, C. Gao, D. Meng, and A. G. Hauptmann, “Decidenet: Counting varying density crowds through attention guided detection and density estimation,” in CVPR , 2018, pp. 5197–5206
2018
Cited alongside, same era.
H. Idrees, M. Tayyab, K. Athrey, D. Zhang, S. Al-Maadeed, N. Rajpoot, and M. Shah, “Composition loss for counting, density map estimation and localization in dense crowds,” in ECCV , 2018, pp. 532–546
2018
Cited alongside, same era.
J. Wan and A. Chan, “Modeling noisy annotations for crowd counting,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
Q. Wang, J. Gao, W. Lin, and X. Li, “Nwpu-crowd: A large-scale benchmark for crowd counting and localization,” PAMI , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, “Understanding convolution for semantic segmentation,” in 2018 IEEE winter conference on applications of computer vision (WACV) . IEEE, 2018, pp. 1451–1460
2018
Cited alongside, same era.
L. Liu, Z. Qiu, G. Li, S. Liu, W. Ouyang, and L. Lin, “Crowd counting with deep structured scale integration network,” in 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019 , 2019, pp. 1774–1783
2019
Cited alongside, same era.
Z. Lin, J. Feng, Z. Lu, Y. Li, and D. Jin, “Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis,” in The Thirty-Third AAAI Conference on Artificial Intelligence . AAAI Press, 2019, pp. 1020–1027
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. Liu, X. Weng, and Y. Mu, “Recurrent attentive zooming for joint crowd counting and precise localization,” in CVPR , 2019, pp. 1217–1226
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
Later among the works it cites.
V. A. Sindagi, R. Yasarla, and V. M. Patel, “Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method,” Technical Report , 2020
2020
Later among the works it cites.
J. Wan, N. S. Kumar, and A. B. Chan, “Fine-grained crowd counting,” IEEE transactions on image processing , vol. 30, pp. 2114–2126, 2021
2021
Closest in time.
J. Cheng, H. Xiong, Z. Cao, and H. Lu, “Decoupled two-stage crowd counting and beyond,” IEEE Transactions on Image Processing , vol. 30, pp. 2862–2875, 2021
2021
Closest in time.
W. Lin, J. Gao, Q. Wang, and X. Li, “Learning to detect anomaly events in crowd scenes from synthetic data,” Neurocomputing , vol. 436, pp. 248–259, 2021
2021
Closest in time.
X. LI and B. ZHAO, “Video distillation,” SCIENCE CHINA Information Sciences , 2021
2021
Closest in time.
2021
Closest in time.
Y. Wang, J. Hou, X. Hou, and L.-P. Chau, “A self-training approach for point-supervised object detection and counting in crowds,” IEEE Transactions on Image Processing , vol. 30, pp. 2876–2887, 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
S. Abousamra, M. Hoai, D. Samaras, and C. Chen, “Localization in the crowd with topological constraints,” 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.