Intelligent Surveillance of Natural Environments
1- Forest Surveillance (3 papers)
T. Boult, R. Micheals, X. Gao, M. Eckmann, "Into the woods: visual surveillance of noncooperative and camouflaged targets in complex outdoor settings",Proceedings of the IEEE, Volume 89, No. 10, pages 1382-1402,October 2001.
M. Shakeri, H. Zhang, "Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences using Low-rank and Invariant Sparse Decomposition", IEEE ICCV 2017, October 2017.
H. Yousif, J. Yuan, R. Kays, Z. He, "Fast Human-Animal Detection from Highly Cluttered Camera-Trap Images using Joint Background Modeling and Deep Learning Classification", IEEE ISCAS 2017, pages 1-4, September 2017.
2-River Surveillance (3 papers)
I. Ali, J. Mille, L. Tougne, "Adding a rigid motion model to foreground detection: Application to moving object detection in rivers", Pattern Analysis and Applications, pages1-20, 2013.
I. Ali, J. Mille, L. Tougne, "Space-time spectral model for object detection in dynamic textured background", Pattern Recognition Letters, Volume 33, Issue 13, pages 1710-1716, 2012.
S. Pereira, J. Maia, “Anomaly Detection in Surveillance Video of Natural Environment”, International Journal of Computer Applications, May 2021.
3-Lake Surveillance (1 paper)
X. Jin, P. Niu, L. Liu,"A GMM-Based Segmentation Method for the Detection of Water Surface Floats", IEEE Access, 2019.
4-Coastal Surveillance (2 papers)
D. Cullen, J. Konrad, T. Little, "Detection and summarization of salient events in coastal environments", IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012, 2012.
D. Cullen, "Detecting and summarizing salient events in coastal videos", TR 2012-06, Boston University, May 2012.
5-Ocean Surveillance (5 papers)
A. Borghgraef, O. Barnich, F. Lapierre, M. Droogenbroeck, W. Philips, M. Acheroy, "An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface", EURASIP Journal on Advances in Signal Processing, 2010.
Z. Szpak, J. Tapamo, "Maritime Surveillance: Tracking Ships inside a Dynamic Background using a Fast Level-Set", Expert Systems with Applications, Volume 38, No. 6, pages 6669-6680, 2011.
D. Prasad, C. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, C. Quek, "Challenges in Video based Object Detection in Maritime Scenario using Computer Vision", WASET International Journal of Computer, Electrical, Automation, Control and Information Engineering, Volume 11, No. 1, January 2017.
D. Prasad, D. Rajan, L. Rachmawati, E. Rajabally, C. Quek, "Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey", IEEE Transactions on Intelligent Transportation Systems, pages 1993–2016, August 2017.
D. Prasad, C. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, C. Quek, “Object Detection in a Maritime Environment:Performance Evaluation of Background Subtraction Methods,” IEEE Transactions on Intelligent Transportations Systems, Volume 20, No. 5, pages 1787–1802, May 2019.
6-Submarine Surveillance (15 papers)
6.1. Swimming pool environments (7 papers)
H. Eng, K. Toh, A. Kam, J. Wang, W. Yau, "An automatic drowning detection surveillance system for challenging outdoorpool environments", IEEE International Conference on Computer Vision, ICCV 2003, pages 532-539, 2003.
H. Eng, K. Toh, A. Kam, J. Wang, W. Yau, "Novel region-based modeling for human detection within highly dynamic aquatic environment", IEEE International Conference onComputer Vision and Pattern Recognition, CVPR 2004, 2004.
L. Fei, W. Xueli, C. Dongsheng, "Drowning Detection Based on Background Subtraction", International Conference on Embedded Software and Systems, ICESS 2009, pages 341-343, 2009.
K. Chan, "Detection of swimmer using dense optical flow motion map and intensity information", Machine Vision and Applications, Volume 24, No. 1, pages 75-101, January 2013.
K. Chan,"Detection of Swimmer Based on Joint Utilization of Motion and Intensity Information", IAPR Conference on Machine Vision Applications, June 2011.
L. Sha, P. Lucey, S. Sridharan, S. Morgan, D. Pease, "Understanding and Analyzing a Large Collection of Archived Swimming Videos", IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pages 674-681, March 2014.
N. Peixoto, N. Cardoso, P. Goncalves, P. Cardoso, J. Cabral, A. Tavares, J. Mendes, "Motion segmentation object detection in complex aquatic scenes and its surroundings", International Conference on Industrial Informatics, INDIN 2012, pages 162-166, 2012.
6.2. Tank environments (1 paper)
S. Abe, T. Takagi, K. Takehara, N. Kimura, T. Hiraishi, K. Komeyama, S. Torisawa, S. Asaumi, "How many fish in a tank? Constructing an automated fish counting system by using PTV analysis", International Congress on High-Speed Imaging and Photonics, 2016.
6.3. Open sea environments (7 papers)
I. Kavasidis, S. Palazzo, "Quantitative Performance Analysis of Object Detection Algorithms on Underwater Video Footage", ACM international workshop on Multimedia Analysis for Ecological Data, MAED 2012, pages 57-60, 2012.
C. Spampinato, S. Palazzo, B. Boom, J. van Ossenbruggen, I. Kavasidis, R. Di Salvo, F. Lin, D. Giordano, L. Hardman, R. Fisher, "Understanding fish behavior during typhoon events in real-life underwater environments", Multimedia Tools and Applications, pages 1-8, 2014.
M. Radolko, F. Farhadifard, U. von Lukas, "Dataset on Underwater Change Detection", IEEE Monterey OCEANS 2016, 2016.
M. Radolko, F. Farhadifard, U. von Lukas, "Change detection in crowded underwater scenes-via an extended Gaussian switch model combined with a flux tensor pre-segmentation", International Conference on Computer Vision Theory and Applications, Volume 5, pages 405-415, 2017.
S. Vasamsetti, S. Setia, N. Mittal, H. Sardana, G. Babba, "Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds", IET Computer Vision, 2018.
P. Patil, O. Thawakar, A. Dudhane, S. Murala, "Motion Saliency based Generative Adversarial Network for Underwater Moving Object Segmentation", IEEE International Conference on Image Processing, ICIP 2019, pages 1565-1569, 2019.
J. Humbert, K. Onthank, K. Williams, "The Open-source Camera Trap for Organism Presence and Underwater Surveillance (OCTOPUS)", HardwareX, 2023.
7. Land environments (1 paper)
Y. Liu, G. Tang, W. Zou, "Video monitoring of Landslide based on background subtraction with Gaussian mixture model algorithm", IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, pages 8432-8435, 2021.