Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning
Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms...
| Main Authors: | Marrable, Daniel, Barker, Kathryn, Tippaya, Sawitchaya, Wyatt, M., Bainbridge, S., Stowar, M., Larke, Jason |
|---|---|
| Format: | Journal Article |
| Published: |
2022
|
| Online Access: | http://hdl.handle.net/20.500.11937/89432 |
Similar Items
Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data
by: Siddiqui, S., et al.
Published: (2018)
by: Siddiqui, S., et al.
Published: (2018)
Fish identification from videos captured in uncontrolled underwater environments
by: Shafait, F., et al.
Published: (2013)
by: Shafait, F., et al.
Published: (2013)
Video based human activities recognition using deep learning
by: Roubleh, A. A., et al.
Published: (2020)
by: Roubleh, A. A., et al.
Published: (2020)
A survey on video face recognition using deep learning
by: Muhammad Firdaus Mustapha,, et al.
Published: (2022)
by: Muhammad Firdaus Mustapha,, et al.
Published: (2022)
Integrating echo-sounder and underwater video data for demersal fish assessment
by: Landero, M., et al.
Published: (2016)
by: Landero, M., et al.
Published: (2016)
The Use of Stationary Underwater Video for Sampling Sharks
by: Harvey, Euan, et al.
Published: (2018)
by: Harvey, Euan, et al.
Published: (2018)
Fish species classification in unconstrained underwater environments based on deep learning
by: Salman, A., et al.
Published: (2016)
by: Salman, A., et al.
Published: (2016)
Badminton smashing recognition through video performance by using deep learning
by: Yip, Zi Ying, et al.
Published: (2022)
by: Yip, Zi Ying, et al.
Published: (2022)
Development of video-based emotion recognition using deep learning with Google colab
by: Gunawan, Teddy Surya, et al.
Published: (2020)
by: Gunawan, Teddy Surya, et al.
Published: (2020)
Abrupt shot boundary detection based on averaged two-dependence estimators learning
by: Tippaya, Sawitchaya, et al.
Published: (2014)
by: Tippaya, Sawitchaya, et al.
Published: (2014)
Combining underwater video methods improves effectiveness of demersal fish assemblage surveys across habitats
by: Logan, J., et al.
Published: (2017)
by: Logan, J., et al.
Published: (2017)
Underwater Image Recognition using Machine Learning
by: Divya, N.K., et al.
Published: (2024)
by: Divya, N.K., et al.
Published: (2024)
Does fish behaviour bias abundance and length information collected by baited underwater video?
by: Coghlan, A., et al.
Published: (2017)
by: Coghlan, A., et al.
Published: (2017)
Baited remote underwater stereo-video outperforms baited downward-facing single-video for assessments of fish diversity, abundance and size composition
by: Cundy, M., et al.
Published: (2017)
by: Cundy, M., et al.
Published: (2017)
Handwriting recognition on library book label
by: Leow, Ee Wen
Published: (2013)
by: Leow, Ee Wen
Published: (2013)
Real-time video segmentation using color information and connected component labeling:application to road sign detection and recognition
by: Lydia, Ubong Jau
Published: (2011)
by: Lydia, Ubong Jau
Published: (2011)
Towards automating underwater measurement of fish length: A comparison of semi-automatic and manual stereo-video measurements
by: Shafait, F., et al.
Published: (2017)
by: Shafait, F., et al.
Published: (2017)
Diversity and Composition of Demersal Fishes along a Depth Gradient Assessed by Baited Remote Underwater Stereo-Video
by: Zintzen, V., et al.
Published: (2012)
by: Zintzen, V., et al.
Published: (2012)
Underwater particle motion (acceleration, velocity and displacement) from recreational swimmers, divers, surfers and kayakers
by: Gavrilov, Alexander, et al.
Published: (2017)
by: Gavrilov, Alexander, et al.
Published: (2017)
A study of discriminant visual descriptors for sport video shot boundary detection
by: Tippaya, S., et al.
Published: (2015)
by: Tippaya, S., et al.
Published: (2015)
Extreme action recognition from real-time video using time-series deep learning model
by: Goh, Qing Hao
Published: (2021)
by: Goh, Qing Hao
Published: (2021)
Pill Recognition Using Minimal Labeled Data
by: Wang, Y., et al.
Published: (2017)
by: Wang, Y., et al.
Published: (2017)
Learning with fewer labels in deep learning for plant phenotyping
by: Chen, Feng
Published: (2022)
by: Chen, Feng
Published: (2022)
Multi-modal Visual Features Based Video Shot Boundary Detection
by: Tippaya, S., et al.
Published: (2017)
by: Tippaya, S., et al.
Published: (2017)
The VISTA deep extragalactic observations (VIDEO) survey
by: Jarvis, M., et al.
Published: (2013)
by: Jarvis, M., et al.
Published: (2013)
Response of diurnal and nocturnal coral reef fish to protection from fishing: An assessment using baited remote underwater video
by: Harvey, Euan, et al.
Published: (2012)
by: Harvey, Euan, et al.
Published: (2012)
Integration of Enhanced Background Filtering and Wavelet Fusion for High Visibility and Detection Rate of Deep Sea Underwater Image of Underwater Vehicle
by: Ahmad Shahrizan, Abdul Ghani, et al.
Published: (2017)
by: Ahmad Shahrizan, Abdul Ghani, et al.
Published: (2017)
Leveraging 3D skeleton video extraction and deep learning for real-time sign language recognition model
by: Ang, Zi Ying
Published: (2024)
by: Ang, Zi Ying
Published: (2024)
Video shot boundary detection based on candidate segment selection and transition pattern analysis
by: Tippaya, S., et al.
Published: (2015)
by: Tippaya, S., et al.
Published: (2015)
Deep learning for emotional speech recognition
by: Alhamada, M. I., et al.
Published: (2020)
by: Alhamada, M. I., et al.
Published: (2020)
Deep face recognition in the wild
by: Yang, Jing
Published: (2022)
by: Yang, Jing
Published: (2022)
Recognition of prior learning: the accelerated rate of change in Australian universities
by: Pitman, Tim
Published: (2009)
by: Pitman, Tim
Published: (2009)
Deep underwater image enhancement through colour cast removal and optimization algorithm
by: Kamil Zakwan, Mohd Azmi, et al.
Published: (2019)
by: Kamil Zakwan, Mohd Azmi, et al.
Published: (2019)
A comparison of calibration methods and system configurations of underwater stereo-video systems for applications in marine ecology
by: Boutros, N., et al.
Published: (2015)
by: Boutros, N., et al.
Published: (2015)
Emotion and mood recognition in response to video
by: Dwi Handayani, Dini Oktarina, et al.
Published: (2015)
by: Dwi Handayani, Dini Oktarina, et al.
Published: (2015)
Spectral Analysis of Estuarine Water for Characterisation of Inherent Optical Properties and Phytoplankton Concentration
by: Marrable, Daniel Stephen
Published: (2018)
by: Marrable, Daniel Stephen
Published: (2018)
A labeled random finite set online multi-object tracker for video data
by: Kim, Du Yong, et al.
Published: (2019)
by: Kim, Du Yong, et al.
Published: (2019)
Underwater Photography
by: N., Mohd Ghazali, et al.
Published: (2011)
by: N., Mohd Ghazali, et al.
Published: (2011)
Deep Learning Using Tiny Domain-Specific Datasets with Sparse Labels
by: Smith, Thomas J
Published: (2021)
by: Smith, Thomas J
Published: (2021)
Face recognition using deep learning
by: Ooi, Zi Xen
Published: (2019)
by: Ooi, Zi Xen
Published: (2019)
Similar Items
-
Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data
by: Siddiqui, S., et al.
Published: (2018) -
Fish identification from videos captured in uncontrolled underwater environments
by: Shafait, F., et al.
Published: (2013) -
Video based human activities recognition using deep learning
by: Roubleh, A. A., et al.
Published: (2020) -
A survey on video face recognition using deep learning
by: Muhammad Firdaus Mustapha,, et al.
Published: (2022) -
Integrating echo-sounder and underwater video data for demersal fish assessment
by: Landero, M., et al.
Published: (2016)