[1] |
TIAN L. Design of sheep number detection system[D]. Hohhot: Inner Mongolia University, 2019: 45-57(in Chinese). |
[2] |
ZHANG L, XU J, TIAN Z, et al. Research and implementation of intelligent counting sheep system in pastoral areas[J]. Telecom Power Technologies, 2017, 34(4): 165-166(in Chinese). |
[3] |
ENZWEILER M, GAVRILA D, GAVRILA D M. Monocular pedestrian detection: Survey and experiments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2179-2195. doi: 10.1109/TPAMI.2008.260 |
[4] |
JONES M J, SNOW D. Pedestrian detection using boosted features over many frames[C]// International Conference on Pattern Recognition. New York, USA: IEEE, 2008: 8-11. |
[5] |
WU B, NEVATIA R. Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors[J]. International Journal of Computer Vision, 2007, 75(2): 247-266. |
[6] |
FELZENSZWALB P F, GIRSHICK R B, McALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. doi: 10.1109/TPAMI.2009.167 |
[7] |
LIU T, TAO D. On the robustness and generalization of cauchy regression[C]// 2014 4th IEEE International Conference on Information Science and Technology (ICIST). New York, USA: IEEE, 2014: 32-37. |
[8] |
ZHAI J Y, TU L Zh, ZHUANG Y. Saliency detection based on boundary prior and adaptive region merging[J]. Computer Engineering and Applications, 2018, 54(6): 178-182(in Chinese). |
[9] |
ZENG L, XU X, CAI B, et al. Multi-scale convolutional neural networks for crowd counting[C]// 2017 IEEE International Conference on Image Processing (ICIP). New York, USA: IEEE, 2017: 89-91. |
[10] |
HUANG S Y, LI X, CHENG Zh Q, et al. Stacked pooling: Improving crowd counting by boosting scale invariance[J]. Computer Vision and Pattern Recognition, 2018(22): 46-52. |
[11] |
ZHANG Y, ZHOU D, CHEN S, et al. Single-image crowd counting via multi-column convolutional neural network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA: IEEE, 2016: 98-103. |
[12] |
WU X, ZHENG Y, YE H, et al. Adaptive scenario discovery for crowd counting[J]. Computer Vision and Pattern Recognition, 2019(9): 12-16. |
[13] |
OORO-RUBIO D, LÓPEZ-SASTRE R J. Towards perspective-free object counting with deep learning[C]// European Conference on Computer Vision(ECCV) 2016. New York, USA: IEEE, 2016: 56-64. |
[14] |
LEI H L. Crowd counting algorithm based on multi model deep convolution network fusion[D]. Hohhot: Inner Mongolia University, 2020: 32-37(in Chinese). |
[15] |
TANG S Y, TAO Y, ZHANG L L, et al. A deep crowd counting algorithm based on multi-column feature map fusion. Journal of Zhengzhou University (Natural Science Edition), 2018, 50(2): 69-74(in Chinese). |
[16] |
WANG Y J, ZHANG W, LIU Y Y, et al. Two-branch fusion network with attention map for crowd counting[J]. Neurocomputing, 2020, 411: 1-8. doi: 10.1016/j.neucom.2020.06.034 |
[17] |
WANG S, LU Y, ZHOU T, et al. SCLNet: Spatial context learning network for congested crowd counting[J]. Neurocomputing, 2020, 404: 227-239. doi: 10.1016/j.neucom.2020.04.139 |
[18] |
WU X, ZHENG Y, YE H, et al. Counting crowds with varying densities via adaptive scenario discovery framework[J]. Neurocomputing. 2020, 397: 127-138. doi: 10.1016/j.neucom.2020.02.045 |
[19] |
LI Y, ZHANG X, CHEN D. CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes[J]. Computer Vision and Pattern Recognition, 2018 (27): 31-39. |
[20] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer Vision and Pattern Recognition, 2014(4): 19-25. |
[21] |
ZHANG C, LI H, WANG X, et al. Cross-scene crowd counting via deep convolutional neural networks[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA: IEEE, 2015: 17-29. |