DisCountNet: Discriminating and counting network for real-time counting and localization of sparse objects in high-resolution UAV imagery

Remote Sensing

Volumn 11, Issue 9, 2019, Pages

  Rahnemoonfar, Maryam ^a   Dobbs, Dugan ^a   Yari, Masoud ^a   Starek, Michael J ^a  

^a TEXAS A AND M UNIVERSITY CORPUS CHRISTI   (United States)

Author keywords

Automatic counting; Deep learning; Real time; UAV

Indexed keywords

ANTENNAS; STATISTICAL TESTS; UNMANNED AERIAL VEHICLES (UAV);

AUTOMATIC COUNTING; COMPETITIVE PERFORMANCE; COUNTING TECHNIQUES; DETECTION NETWORKS; HIGH RESOLUTION IMAGE; REAL TIME; SPARSE DISTRIBUTION; SPARSE REPRESENTATION;

DEEP LEARNING;

EID: 85065727903     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs11091128     Document Type: Article

References (52)

1. Chan, A.B.; Liang, Z.S.J.; Vasconcelos, N. Privacy preserving crowd monitoring: Counting people without people models or tracking. In Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 23-28 June 2008; pp. 1-7
2. Idrees, H.; Saleemi, I.; Seibert, C.; Shah, M. Multi-source multi-scale counting in extremely dense crowd images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 23-28 June 2013; pp. 2547-2554
3. Shen, Z.; Xu, Y.; Ni, B.;Wang, M.; Hu, J.; Yang, X. Crowd counting via adversarial cross-scale consistency pursuit. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 5245-5254
4. Boominathan, L.; Kruthiventi, S.S.; Babu, R.V. Crowdnet: A deep convolutional network for dense crowd counting. In Proceedings of the 24th ACM international conference on Multimedia, Amsterdam, The Netherlands, 15-19 October 2016; pp. 640-644
5. Liu, J.; Gao, C.; Meng, D.; Hauptmann, A.G. Decidenet: Counting varying density crowds through attention guided detection and density estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 5197-5206
6. Guerrero-Gómez-Olmedo, R.; Torre-Jiménez, B.; López-Sastre, R.; Maldonado-Bascón, S.; Onoro-Rubio, D. Extremely overlapping vehicle counting. In Pattern Recognition and Image Analysis; Springer: Cham, Switzerland, 2015; pp. 423-431
7. Xie, W.; Noble, J.A.; Zisserman, A. Microscopy cell counting and detection with fully convolutional regression networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 2018, 6, 283-292
8. Rahnemoonfar, M.; Sheppard, C. Deep count: Fruit counting based on deep simulated learning. Sensors 2017, 17, 905
9. Rahnemoonfar, M.; Sheppard, C. Real-time yield estimation based on deep learning. Proc. SPIE 2017, 10218
10. Huang, L.C.; Kulkarni, K.; Jha, A.; Lohit, S.; Jayasuriya, S.; Turaga, P. CS-VQA: Visual Question Answering with Compressively Sensed Images. In Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7-10 October 2018; pp. 1283-1287
11. Fukui, A.; Park, D.H.; Yang, D.; Rohrbach, A.; Darrell, T.; Rohrbach, M. Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv 2016, arXiv:1606.01847
12. Sheppard, C.; Rahnemoonfar, M. Real-time scene understanding for UAV imagery based on deep convolutional neural networks. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), FortWorth, TX, USA, 23-28 July 2017; pp. 2243-2246
13. Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 7263-7271
14. Lin, T.Y.; Goyal, P.; Girshick, R.; He, K.; Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017; pp. 2980-2988
15. Kamangir, H.; Rahnemoonfar, M.; Dobbs, D.; Paden, J.; Fox, G.C. Detecting ice layers in Radar images with deep hybrid networks. In Proceedings of the IEEE Conference on Geoscience and Remote Sensing (IGARSS), Valencia, Spain, 22-27 July 2018
16. Rahnemoonfar, M.; Robin, M.; Miguel, M.V.; Dobbs, D.; Adams, A. Flooded area detection from UAV images based on densely connected recurrent neural networks. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), FortWorth, TX, USA, 23-28 July 2017; pp. 3743-3746
17. Chattopadhyay, P.; Vedantam, R.; Selvaraju, R.R.; Batra, D.; Parikh, D. Counting everyday objects in everyday scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 1135-1144
18. Zhang, C.; Li, H.;Wang, X.; Yang, X. Cross-scene crowd counting via deep convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 833-841
19. Hu, P.; Ramanan, D. Finding tiny faces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 951-959
20. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems 28; Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2015; pp. 91-99
21. Zhang, Y.; Zhou, D.; Chen, S.; Gao, S.; Ma, Y. Single-image crowd counting via multi-column convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June-1 July 2016; pp. 589-597
22. Fiaschi, L.; Köthe, U.; Nair, R.; Hamprecht, F.A. Learning to count with regression forest and structured labels. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan, 11-15 November 2012; pp. 2685-2688
23. Sam, D.B.; Surya, S.; Babu, R.V. Switching convolutional neural network for crowd counting. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017; pp. 4031-4039
24. Idrees, H.; Tayyab, M.; Athrey, K.; Zhang, D.; Al-Maadeed, S.; Rajpoot, N.; Shah, M. Composition loss for counting, density map estimation and localization in dense crowds. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September 2018; pp. 532-546
25. Walach, E.; Wolf, L. Learning to count with cnn boosting. In Computer Vision-ECCV 2016; Springer: Cham, Switzerland, 2016; pp. 660-676
26. Deb, D.; Ventura, J. An aggregated multicolumn dilated convolution network for perspective-free counting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18-22 June 2018; pp. 195-204
27. Wawerla, J.; Marshall, S.; Mori, G.; Rothley, K.; Sabzmeydani, P. Bearcam: Automated wildlife monitoring at the arctic circle. Mach. Vis. Appl. 2009, 20, 303-317
28. Shang, C.; Ai, H.; Bai, B. End-to-end crowd counting via joint learning local and global count. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25-28 September 2016; pp. 1215-1219
29. Liu, X.; van de Weijer, J.; Bagdanov, A.D. Leveraging unlabeled data for crowd counting by learning to rank. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 7661-7669
30. Babu Sam, D.; Sajjan, N.N.; Venkatesh Babu, R.; Srinivasan, M. Divide and grow: Capturing huge diversity in crowd images with incrementally growing CNN. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 3618-3626
31. Shi, Z.; Zhang, L.; Liu, Y.; Cao, X.; Ye, Y.; Cheng, M.M.; Zheng, G. Crowd counting with deep negative correlation learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 5382-5390
32. Tayara, H.; Soo, K.G.; Chong, K.T. Vehicle detection and counting in high-resolution aerial images using convolutional regression neural network. IEEE Access 2018, 6, 2220-2230
33. Chen, S.W.; Shivakumar, S.S.; Dcunha, S.; Das, J.; Okon, E.; Qu, C.; Taylor, C.J.; Kumar, V. Counting apples and oranges with deep learning: A data-driven approach. IEEE Robot. Autom. Lett. 2017, 2, 781-788
34. Lu, H.; Cao, Z.; Xiao, Y.; Zhuang, B.; Shen, C. TasselNet: Counting maize tassels in the wild via local counts regression network. Plant Methods 2017, 13, 79
35. Chamoso, P.; Raveane, W.; Parra, V.; González, A. UAVs applied to the counting and monitoring of animals. In Ambient Intelligence-Software and Applications; Springer: Cham, Switzerland, 2014; pp. 71-80
36. Rivas, A.; Chamoso, P.; González-Briones, A.; Corchado, J. Detection of Cattle Using Drones and Convolutional Neural Networks. Sensors 2018, 18, 2048
37. Longmore, S.; Collins, R.; Pfeifer, S.; Fox, S.; Mulero-Pázmány, M.; Bezombes, F.; Goodwin, A.; De Juan Ovelar, M.; Knapen, J.; Wich, S. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. Int. J. Remote Sens. 2017, 38, 2623-2638
38. Koskowich, B.J.; Rahnemoonfar, M.; Starek, M. Virtualot-A Framework Enabling Real-Time Coordinate Transformation & Occlusion Sensitive Tracking Using UAS Products, Deep Learning Object Detection & Traditional Object Tracking Techniques. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Valencia, Spain, 22-27 July 2018; pp. 6416-6419
39. Wallace, L.; Lucieer, A.;Watson, C.; Turner, D. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 2012, 4, 1519-1543
40. d'Oleire Oltmanns, S.; Marzolff, I.; Peter, K.; Ries, J. Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sens. 2012, 4, 3390-3416
41. Chabot, D.; Bird, D.M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in? J. Unmanned Veh. Syst. 2015, 3, 137-155
42. Barbedo, J.G.A.; Koenigkan, L.V. Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook Agric. 2018, 47, 214-222
43. Jin, X.; Davis, C.H. Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks. Image Vis. Comput. 2007, 25, 1422-1431
44. Jiang, Q.; Cao, L.; Cheng, M.; Wang, C.; Li, J. Deep neural networks-based vehicle detection in satellite images. In Proceedings of the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), Beijing, China, 14-17 October 2015; pp. 184-187
45. Miyamoto, H.; Uehara, K.; Murakawa, M.; Sakanashi, H.; Nasato, H.; Kouyama, T.; Nakamura, R. Object Detection in Satellite Imagery Using 2-Step Convolutional Neural Networks. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium(IGARSS), Valencia, Spain, 22-27 July 2018; pp. 1268-1271
46. Starek, M.J.; Davis, T.; Prouty, D.; Berryhill, J. Small-scale UAS for geoinformatics applications on an island campus. In Proceedings of the 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Corpus Christi, TX, USA, 20-21 November 2014; pp. 120-127
47. Lowe, D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91-110
48. United States Department of Agriculture. Balancing Animals with Your Forage; United States Department of Agriculture: Washington, DC, USA, 2009
49. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015; Springer: Cham, Switzerland, 2015; pp. 234-241
50. Li, Y.; Zhang, X.; Chen, D. Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 1091-1100
51. Wang, Z.; Simoncelli, E.P.; Bovik, A.C. Multiscale structural similarity for image quality assessment. In Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 9-12 November 2003; Volume 2, pp. 1398-1402
52. Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv 2014, arXiv:1412.6980