Birds (11 papers)
T. Ko, S. Soatto, D. Estrin, "Background subtraction on distributions", European Conference on Computer Vision, ECCV 2008, pages 222–230, October 2008.
T. Ko, S. Soatto, D. Estrin, "Warping background subtraction", IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2010, June 2010.
M. Shakeri, H. Zhang, "Real-time bird detection based on background subtraction", World Congress on Intelligent Control and Automation, WCICA 2012, pages 4507-4510, July 2012.
P. Dickinson, R. Freeman, S. Patrick, S. Lawson,Autonomous Monitoring of Cliff Nesting Seabirds using Computer Vision", International Workshop on Distributed Sensing and Collective Intelligence in Biodiversity Monitoring, 2008.
P. Dickinson, "Monitoring the Vulnerable using Automated Visual Surveillance", PhD thesis, University of Lincoln, UK, 2008.
P. Dickinson, C. Qing, S. Lawson, R. Freeman, "Automated Visual Monitoring of Nesting Seabirds", ICPR Workshop on Visual Observation and Analysis of Animal and Insect Behavior, VAIB 2010, 2010.
E. Simons, M. Hindersn, "Automatic counting of birds in a bird deterrence field trial", WILEY Ecology and Evolution, 2019.
B. Weinstein, "Motionmeerkat: integrating motion video detection and ecological monitoring", Methods in Ecology and Evolution, 2014.
B. Weinstein, "A computer vision for animal ecology", Journal of Animal Ecology, October 2017.
K. Goehner, T. Desell and al."A Comparison of Background Subtraction Algorithms for Detecting Avian Nesting Events in Uncontrolled Outdoor Video", IEEE International Conference on e-Science, pages 187-195, 2015.
K. Goehner, T. Desell and al, "On the effectiveness of crowd sourcing avian nesting video analysis at WildlifeHome", Procedia Computer Science", pages 384-393, 2015.
Fish (27 papers)
F. El Baf, T. Bouwmans. Comparison of background subtraction methods for a multimedia learning space. International Conference on Signal Processing and Multimedia, SIGMAP, July 2007.
C. Spampinato, Y. Burger, G. Nadarajan, R. Fisher, "Detecting, tracking and counting fish in low quality unconstrained underwater videos", VISAPP 2008, pages 514–519, 2008.
C. Spampinato, S. Palazzo, I. Kavasidis, "A texton-based kernel density estimation approach for background modeling under extreme conditions", Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 74-83, 2014.
X. Zhao, S. Yan, Q. Gao, “An Algorithm for Tracking Multiple Fish Based on Biological Water Quality Monitoring”, IEEE Access, 2019.
A. Salman, S. Maqbool, A. Khan,A. Jalal, F. Shafait, “Real-time fish detection in complex backgrounds using probabilistic background modelling”, Ecological Informatics Volume 51, pages 44-51 May 2019,
A. Salman, S. Siddiqui, F. Shafait, A. Mian, M. Shortis, K. Khurshid, A. Ulges, U. Schwanecke, "Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system", ICES Journal of Marine Science, 2019.
Y. Ling, P. Lau "Fish monitoring in complex environment", International Workshop on Advanced Image Technology, IWAIT 2019, 2019.
A. Jalal, A. Salaman, A. Mian, M. Shortis, F. Shafait, "Fish detection and species classification in underwater environments using deep learning with temporal information", Ecological Informatics, Volume 57, May 2020.
V. Shevchenko, "Fish detection for species recognition", Master Thesis, Lappeenranta University of Technology, Finland, 2017.
C. Negrea, D. Thompson, S. Juhnke, S. Fryer, F. Loge, "Automated Detection and Tracking of Adult Pacific Lampreys in Underwater Video Collected at Snake and Columbia River Fishways", North American Journal of Fisheries Management, pages 111-118, 2014.
S. Panda, P. Nanda, "Kernel Density Estimation and Correntropy based Background Modeling and Camera Model Parameter Estimation for Underwater Video Object Detection", Preprint, 2021.
M. Shruthi, B.Harsha, G. Indumathi, “Sliding Windowed Fuzzy Correlation Analysis-Based Marine Motion Detection”, Conference on High Performance Computing and Networking, 2022.
D. Rout, P. Bhatt, B. Subudhi, T. Veerakumar, S. Chaudhury, “A Novel Five-frame Difference Scheme for Local Change Detection in Under Water Video”, IEEE ICIIP, pages 1-6, Shimla, India, 2017.
D. Rout, B. Subudhi, T. Veerakumar, S. Chaudhury, “Spatio-Contextual Gaussian Mixture Model for Local Change Detection in Underwater Video", Expert Systems with Applications, Volume 97, pages 117-136, 2018.
A. Salman, S. Maqbool, A. Khan, A. Jalal, F. Shafait, “Real-time Fish Detection in Complex Backgrounds using Probabilistic Background Modelling”, Ecological Informatics, Volume 51, pages 44-51, May 2019.
A. Salman, S. Siddiqui, F. Shafait, A. Mian, M. Shortis, K. Khurshid, A. Ulges, U. Schwanecke, “Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system”, ICES Journal of Marine Science, Volume 77, Issue 4, pages 1295-1307, 2020.
V. Bajpai, A. Sharma, B. Subudhi, T. Veerakumar, V. Jakhetiya, "Underwater U-Net: Deep Learning with U-Net for Visual Underwater Moving Object Detection", OCEANS 2021, San Diego, USA, pages 1-4, September 2021.
M. Kapoor, S. Patra, B. Subudhi, V. Jakhetiya, A. Bansal, "Underwater Moving Object Detection using an End-to-End Encoder-Decoder Architecture and GraphSage With Aggregator and Refactoring", WiCV Workshop in conjunction with IEEE Conference on Computer Vision and Pattern Recognition, CVPRW 2023, Vancouver, Canada, 2023.
A. Srikanth, S. Thirandas, D. Balamuguran, A. Daga, R. Vincent, T. Padir, D. Saha, “Real-Time Background-Agnostic Fish Localization in Underwater Videos towards Autonomous Species Monitoring”, IEEE Oceans, 2024.
A. Zhang, T. Palaoag, “Underwater Fish Tracking Algorithm Based on ViBe Detection and Covariance Matrix”, International Conference on Image, Video and Signal Processing, IVSP 2024, March 2024.
M. Nissar, B. Subudhi, V. Jakhetiya, A. Mishra, "Underwater Change Detection using Multiple Sampling-based Probabilistic Learner and Feature Preservance Discriminator", IEEE International Conference on Image Processing, ICIP 2024, Abu Dhabi, UAE, October 2024.
M. Nissar, A. Mishra, B. Subudhi, “Dual Stream Encoder–Decoder Architecture with Feature Fusion Model for Underwater Object Detection”, MDPI Mathematics, October 2024.
M. Kapoor, B. Subudhi, V. Jakhetiya A. Bansal, "Principal Graph Neighborhood Aggregation for Underwater Moving Object Detection", International Conference on Pattern Recognition , ICPR 2024, Kolkata, India, October 2024.
D. Rout M. Kapoor, B. Subudhi, V. Thangaraj, V. Jakhetiya, A. Bansal, "Underwater Visual Surveillance: A Comprehensive Survey", Ocean Engineering , October 2024.
M. Kapoor, B. Subudhi, A. Bansal, "Graph Refinement in Latent Space: A Hypergraph Convolution for Underwater Object Detection", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2025, Hyderabad, India, 2025.
M. Nissar, B. Subudhi, A. Mishra, V. Jakhetiya, “NICASU: Neurotransmitter Inspired Cognitive AI Architecture for Surveillance Underwater”, IEEE Transactions on Artificial Intelligence, March 2025.
M. Kapoor, B. Subudhi, V. Jakhetiya, A. Bansal, “MD-UMOS: Markov Diffusion Induced Spectral Graph Learning for Underwater Moving Object Segmentation”, IEEE Transactions on Information Forensics and Security, 2025.
Honeybees (6 papers)
M. Himmelsbach, U. Knauer, F.Winkler, F. Zautke, K. Bienefeld, B. Meffert, "Application of an adaptive background model for monitoring honeybees", VIIP 2005, 2005.
J. Campbell, L. Mummert, R. Sukthankar, "Video monitoring of honey bee colonies at the hive entrance", ICPR Workshop on Visual Observation and Analysis of Animal and Insect Behavior, VAIB 2008, December 2008.
T. Kimura, M. Ohashi, K. Crailsheim, T. Schmickl, R. Odaka, H. Ikeno, "Tracking of multiple honey bees on a flat surface", International Conference on Emerging Trends in Engineering and Technology, ICETET 2012, pages 36–39, November 2012.
Z. Babic, R. Pilipovic, V. Risojevic, G. Mirjanic, "Pollen bearing honey bee detection in hive entrance video recorded by remote embedded system for pollination monitoring", ISPRS Congress, July 2016.
R. Pilipovic, V. Risojevic, Z. Babic, G. Mirjanic, "Background subtraction for honey bee detection in hive entrance video", INFOTEH-JAHORINA, Volume 15, March 2016.
C. Yang, "The Use of Video to Detect and Measure Pollen on Bees Entering a Hive", PhD Thesis, Auckland University of Technology, 2017.
Spiders (1 paper)
Y. Iwatani, K. Tsurui, A. Honm, "Position and Direction Estimation of Wolf Spiders, Pardosa astrigera, from Video Images", International Conference on Robotics and Biomimetics, ICRB 2016, December 2016.
Lizards (1 paper)
Y. Nguwi, A. Kouzani, J. Kumar, D. Driscoll, "Automatic detection of lizards", International Conference on Advanced Mechatronic Systems, ICAMechS 2006,
pages 300-305, 2016.
Hinds (1 paper)
P. Khorrami, J Wang, and T Huang, "Multiple animal species detection using robust principal component analysis and large displacement optical flow", Workshop on Visual Observation and Analysis of Animal and Insect Behavior, VAIB 2012, 2012.
Miscellaneous Animals (18 papers)
M. Shakeri, H. Zhang, "Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences using Low-rank and Invariant Sparse Decomposition, IEEE International Conference on Computer Vision, 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.
M. Janzen, K. Visser, D. Visscher, I. Mac Leod, D. Vujnovic, K. Vujnovic, "Semi-automated camera trap image processing for the detection of ungulate fence crossing events", Environmental Monitoring and Assessment, Volume 189, No. 10, 2017.
W. Huang, Q. Zeng, M. Chen, "Motion characteristics estimation of animals in video surveillance", IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017, pages 1098-1102, 2017.
B. Weinstein, "Scene-specific convolutional neural networks for video-based biodiversity detection", Methods in Ecology and Evolution, 2018.
W. Hong, A. Kennedy, X. Burgos-Artizzu, M. Zelikowsky, S. Navonne, P. Perona, D. Anderson, "Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning", PNAS, 2015.
S. Beery, G. Horn, P. Peron, "Recognition in Terra Incognita", European Conference on Computer Vision, ECCV 2018, 2018.
X. Yu , J. Wang, R. Kays, P. Jansen, T. Wang, T. Huang, "Automated identification of animal species in camera trap images", EURASIP Journal on Image and Video Processing, 2013.
M. Wilber, W. Scheirer, P. Leitner, B. Heflin, J. Zott, D. Reinke, D. Delaney, T. Boult, "Animal recognition in the mojave desert: Vision tools for field biologists", IEEE WACV 2013, pages 206-213, 2013.
K. Zhao, D. He, "Target detection method for moving cows based on background subtraction", International Journal of Agricultural and Biological Engineering, 2015.
R. Nishide, Y. Hosomi, T. Ohkawa, K. Oyama, C. Ohta, "Detecting and Tracking Breeding Cows from Bird’s Eye Video of Pasture", IIAE International Conference on Intelligent Systems and Image Processing, pages 239-246, 2017.
M. Aotani, R. Nishide, Y. Takaki, C. Ohta, K. Oyama, "Refined Cattle Detection using Composite Background Subtraction and Brightness Intensity from Bird’s Eye Images", "International Symposium on Information and Communication Technology, SoICT 2018, December 2018.
W. Huang, Q. Zeng , M. Chen, "Motion characteristics estimation of animals in video surveillance", IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017, pages 1098-1102, 2017.
C. Cohen, D. Haanpaa, J. Zott, "Machine vision algorithms for robust animal species identification", IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2015, pages 1-7, 2015.,
C. Cohen, D. Haanpaa, S. Rowe, J. Zott, "Vision algorithms for automated census of animals", IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2011, pages 1-5, 2011.
E. Sheehan, D. Bridger, S. Nancollas, S. Pittman, "PelagiCam: a novel underwater imaging system with computer vision for semi-automated monitoring of mobile marine fauna at offshore structures", Environmental Monitoring and Assessment, 2020.
M. Lv, H. Liu, M. Wang, Q. Li, “Sheep detection in grassland using deep learning and background modelling”, International Conference on Man-Machine-Environment System Engineering, MMESE 2023, October 2023.
R. Wang, W. Noguchi, E. Zhang, K. Osada, M. Yamamoto, Robust Animal Tracking and Stereotypical Behavior Detection Under Real Environment Using Temporal Averaging Background Subtraction, Intelligent Systems Conference, pages 857-875, 2023.