Fish activity tracking and species identification in underwater video

Ekram Hossain; SM Shaiful Alam; Amin Ahsan Ali; M Ashraful Amin

doi:10.1109/iciev.2016.7760189

Conference Paper2016Cited by 44

Fish activity tracking and species identification in underwater video

Ekram Hossain, SM Shaiful Alam, Amin Ahsan Ali, M Ashraful Amin

2016 5th International conference on informatics, electronics and vision (ICIEV)

IEEE, pp. 62–66

DOI: 10.1109/iciev.2016.7760189 URL

Abstract

In this paper we propose an automatic marine life monitoring system. First task in the monitoring process is to detect underwater moving objects as fishes. Second Task is to identify the species of the detected fish. Third task is to track the detected fish to avoid multiple counting and record their activities. Detection is performed using GMM based background subtraction method, classification is performed using Pyramid Histogram Of visual Words (PHOW) features with SVM classifier and finally identified fishes are tracked using “Kalman Filter”. This experiment is performed using data-set from the CLEF 2015. The proposed system can detect and track fishes with 48.94 percent accuracy in videos, and it can identify fishes in high resolution still image with 91.7 percent accuracy where as in the low quality video fishes are detected with 40.1 percent accuracy.

Keywords

Computer scienceArtificial intelligenceComputer visionHistogramBackground subtractionSupport vector machinePattern recognition (psychology)UnderwaterKalman filterClassifier (UML)

CCDS Authors

Ekram Hossain

Collaborator, HCI Wing

Research Data Engineer II

University of Rochester Medical Center

Amin Ahsan Ali, PhD

Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Artificial Intelligence, Machine Learning

M Ashraful Amin, PhD

Founder Director, CCDS
Director, AI/ML, HCI & DS Wings

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

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17.Pierre-Hugues Joalland, Sébastien Paris, Hervé Glotin. (2014). Efficient instance-based fish species visual identification by global representation. CLEF (Working Notes), 785–789

Cited By

1.Ling Yang, Yeqi Liu, Huihui Yu, Xiaomin Fang, Lihua Song, Daoliang Li, Yingyi Chen. (2020). Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review. Archives of Computational Methods in Engineering, 28(4), 2785–2816[10.1007/s11831-020-09486-2]
2.Daoliang Li, Yinfeng Hao, Yanqing Duan. (2019). Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. Reviews in Aquaculture, 12(3), 1390–1411[10.1111/raq.12388]
3.Saleh, Alzayat, Sheaves, Marcus, Azghadi, Mostafa Rahimi. (2022). Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey. arXiv (Cornell University)[10.1111/faf.12666]
4.Mutasem K. Alsmadi, Ibrahim Almarashdeh. (2020). A survey on fish classification techniques. Journal of King Saud University - Computer and Information Sciences, 34(5), 1625–1638[10.1016/j.jksuci.2020.07.005]
5.Alexa Sugpatan Abangan, Dorothée Kopp, Robin Faillettaz. (2023). Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity. Frontiers in Marine Science, 10[10.3389/fmars.2023.1010761]
6.Yupeng Mei, Boyang Sun, Daoliang Li, Huihui Yu, Hanxiang Qin, Huihui Liu, Ni Yan, Yingyi Chen. (2022). Recent advances of target tracking applications in aquaculture with emphasis on fish. Computers and Electronics in Agriculture, 201, 107335[10.1016/j.compag.2022.107335]
7.Vanesa Lopez-Vazquez, Jose Manuel Lopez-Guede, Damianos Chatzievangelou, Jacopo Aguzzi. (2023). Deep learning based deep-sea automatic image enhancement and animal species classification. Journal Of Big Data, 10(1)[10.1186/s40537-023-00711-w]
8.Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne. (2017). Fish Classification in Context of Noisy Images. Communications in computer and information science, 216–226[10.1007/978-3-319-65172-9_19]
9.Ramil Lumauag, Marevic Nava. (2018). Fish Tracking and Counting using Image Processing. , 1–4[10.1109/hnicem.2018.8666369]
10.Cyril Jay L. Chan, Ethan James A. Reyes, Noel B. Linsangan, Roben A. Juanatas. (2022). Real-time Detection of Aquarium Fish Species Using YOLOv4-tiny on Raspberry Pi 4. 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 1–6[10.1109/iicaiet55139.2022.9936790]
11.Krithika M. Pai, K. B. Ajitha Shenoy, M. M. Manohara Pai. (2022). A Computer Vision Based Behavioral Study and Fish Counting in a Controlled Environment. IEEE Access, 10, 87778–87786[10.1109/access.2022.3197887]
12.Junhui Wang, Meng Zhao, Li Zou, Yi Hu, Xuezhen Cheng, Xiaofeng Liu. (2019). Fish Tracking Based on Improved TLD Algorithm in Real-World Underwater Environment. Marine Technology Society Journal, 53(3), 80–89[10.4031/mtsj.53.3.8]
13.Universitas Dian Nuswantoro, Ricardus Pramunendar, Dwi Prabowo, Universitas Dian Nuswantoro, Dewi Pergiwati, Universitas Dian Nuswantoro, Yuslena Sari, Universitas Lambung Mangkurat, Pulung Andono, Universitas Dian Nuswantoro, Moch Soeleman, Universitas Dian Nuswantoro. (2020). New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique. International journal of intelligent engineering and systems, 13(4), 293–304[10.22266/ijies2020.0831.26]
14.Jianhao Yin, Junfeng Wu, Chunqi Gao, Zhongai Jiang. (2022). LIFRNet: A Novel Lightweight Individual Fish Recognition Method Based on Deformable Convolution and Edge Feature Learning. Agriculture, 12(12), 1972[10.3390/agriculture12121972]
15.Universitas Gadjah Mada, Ricardus Pramunendar, Sunu Wibirama, Universitas Gadjah Mada, Paulus Santosa, Universitas Gadjah Mada, Pulung Andono, Universitas Dian Nuswantoro, Moch Soeleman, Universitas Dian Nuswantoro. (2019). A Robust Image Enhancement Techniques for Underwater Fish Classification in Marine Environment. International journal of intelligent engineering and systems, 12(5), 116–129[10.22266/ijies2019.1031.12]
16.K Banno, H Kaland, AM Crescitelli, SA Tuene, GH Aas, LC Gansel. (2022). A novel approach for wild fish monitoring at aquaculture sites: wild fish presence analysis using computer vision. Aquaculture Environment Interactions, 14, 97–112[10.3354/aei00432]
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18.Alberto Maximiliano Crescitelli, Lars Christian Gansel, Houxiang Zhang. (2021). NorFisk: fish image dataset from Norwegian fish farms for species recognition using deep neural networks. Modeling Identification and Control A Norwegian Research Bulletin, 42(1), 1–16[10.4173/mic.2021.1.1]
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24.Li Zou, Meng Zhao, Fangfang Cao, Shiliang Zan, Xuezhen Cheng, Xiaofeng Liu. (2021). Fish Tracking Based on Feature Fusion and Scale Adaptation in a Real-World Underwater Environment. Marine Technology Society Journal, 55(2), 45–53[10.4031/mtsj.55.2.12]
25.Sudhakara Malla, M. Janaki Meena, Obulakonda Reddy. R, V. Mahalakshmi, Awatef Balobaid. (2022). A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images. International Journal on Recent and Innovation Trends in Computing and Communication, 10(4), 01–09[10.17762/ijritcc.v10i4.5524]
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← All publications

Abstract

CCDS Authors

Ekram Hossain

Collaborator, HCI Wing

Research Data Engineer II

University of Rochester Medical Center

Amin Ahsan Ali, PhD

Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Artificial Intelligence, Machine Learning

M Ashraful Amin, PhD

Founder Director, CCDS
Director, AI/ML, HCI & DS Wings

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

1.David Lowe. (1999). Object recognition from local scale-invariant features. , 1150–1157 vol.2[10.1109/iccv.1999.790410]

2.Svetlana Lazebnik, C. Schmid, Jean Ponce. (2006). Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. , 2, 2169–2178[10.1109/cvpr.2006.68]

3.Anna Bosch, Andrew Zisserman, Xavier Muñoz. (2007). Image Classification using Random Forests and Ferns. , 1–8[10.1109/iccv.2007.4409066]

4.Oncel Tuzel, Fatih Porikli, Peter Meer. (2006). Region Covariance: A Fast Descriptor for Detection and Classification. Lecture notes in computer science, 589–600[10.1007/11744047_45]

5.John G. Allen, Richard Yi Da Xu, Jesse S. Jin. (2004). Object tracking using CamShift algorithm and multiple quantized feature spaces. , 3–7

6.Mohinder S. Grewal, Angus P. Andrews. (2014). Kalman Filtering. [10.1002/9781118984987]

7.Concetto Spampinato, Yun-Heh Chen-Burger, Gayathri Devi Nadarajan, Robert B. Fisher. (2008). DETECTING, TRACKING AND COUNTING FISH IN LOW QUALITY UNCONSTRAINED UNDERWATER VIDEOS. , 514–519[10.5220/0001077705140519]

8.Concetto Spampinato, Daniela Giordano, Roberto Di Salvo, Yun-Heh Chen-Burger, Robert B. Fisher, Gayathri Devi Nadarajan. (2010). Automatic fish classification for underwater species behavior understanding. , 45–50[10.1145/1877868.1877881]

9.Mauricio Villegas, Henning Müller, Andrew Gilbert, Luca Piras, Josiah Wang, Krystian Mikolajczyk, Alba García Seco de Herrera, Stefano Bromuri, M. Ashraful Amin, Mahmood Kazi Mohammed, Burak Acar, Suzan Üsküdarlı, Neda Barzegar Marvasti, José F. Aldana, María del Mar Roldán‐García. (2015). General Overview of ImageCLEF at the CLEF 2015 Labs. Lecture notes in computer science, 444–461[10.1007/978-3-319-24027-5_45]

10.Fatih Porikli. (2006). Achieving real-time object detection and tracking under extreme conditions. Journal of Real-Time Image Processing, 1(1), 33–40[10.1007/s11554-006-0011-z]

11.Phoenix X. Huang, Bastiaan J. Boom, Robert B. Fisher. (2013). Underwater Live Fish Recognition Using a Balance-Guaranteed Optimized Tree. Lecture notes in computer science, 422–433[10.1007/978-3-642-37331-2_32]

12.Concetto Spampinato, Simone Palazzo, Bastian J. Boom, Jacco van Ossenbruggen, Isaak Kavasidis, Roberto Di Salvo, Fang‐Pang Lin, Daniela Giordano, Lynda Hardman, Robert B. Fisher. (2012). Understanding fish behavior during typhoon events in real-life underwater environments. Multimedia Tools and Applications, 70(1), 199–236[10.1007/s11042-012-1101-5]

13.(2012). COVARIANCE BASED FISH TRACKING IN REAL-LIFE UNDERWATER ENVIRONMENT. , 409–414[10.5220/0003866604090414]

14.Bastiaan J. Boom, Phoenix X. Huang, Cigdem Beyan, Concetto Spampinato, Simone Palazzo, Jiyin He, Emma Beauxis-Aussalet, Sun-In Lin, Hsiu‐Mei Chou, Gayathri Devi Nadarajan, Jessica Chen-Burger, Jacco van Ossenbruggen, Daniela Giordano, Lynda Hardman, Fang‐Pang Lin, Bob Fisher. (2012). Long-term underwater camera surveillance for monitoring and analysis of fish populations. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands[link]

15.M. Ashraful Amin, Mahmood Kazi Mohammed. (2015). Overview of the ImageCLEF 2015 medical clustering task. CLEF (Working Notes)

16.Katy Blanc, Diane Lingrand, Fŕed́eric Precioso. (2014). Fish Species Recognition from Video using SVM Classifier. , 1–6[10.1145/2661821.2661827]

17.Pierre-Hugues Joalland, Sébastien Paris, Hervé Glotin. (2014). Efficient instance-based fish species visual identification by global representation. CLEF (Working Notes), 785–789

Cited By

1.Ling Yang, Yeqi Liu, Huihui Yu, Xiaomin Fang, Lihua Song, Daoliang Li, Yingyi Chen. (2020). Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review. Archives of Computational Methods in Engineering, 28(4), 2785–2816[10.1007/s11831-020-09486-2]

2.Daoliang Li, Yinfeng Hao, Yanqing Duan. (2019). Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. Reviews in Aquaculture, 12(3), 1390–1411[10.1111/raq.12388]

3.Saleh, Alzayat, Sheaves, Marcus, Azghadi, Mostafa Rahimi. (2022). Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey. arXiv (Cornell University)[10.1111/faf.12666]

4.Mutasem K. Alsmadi, Ibrahim Almarashdeh. (2020). A survey on fish classification techniques. Journal of King Saud University - Computer and Information Sciences, 34(5), 1625–1638[10.1016/j.jksuci.2020.07.005]

5.Alexa Sugpatan Abangan, Dorothée Kopp, Robin Faillettaz. (2023). Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity. Frontiers in Marine Science, 10[10.3389/fmars.2023.1010761]

6.Yupeng Mei, Boyang Sun, Daoliang Li, Huihui Yu, Hanxiang Qin, Huihui Liu, Ni Yan, Yingyi Chen. (2022). Recent advances of target tracking applications in aquaculture with emphasis on fish. Computers and Electronics in Agriculture, 201, 107335[10.1016/j.compag.2022.107335]

7.Vanesa Lopez-Vazquez, Jose Manuel Lopez-Guede, Damianos Chatzievangelou, Jacopo Aguzzi. (2023). Deep learning based deep-sea automatic image enhancement and animal species classification. Journal Of Big Data, 10(1)[10.1186/s40537-023-00711-w]

8.Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne. (2017). Fish Classification in Context of Noisy Images. Communications in computer and information science, 216–226[10.1007/978-3-319-65172-9_19]

9.Ramil Lumauag, Marevic Nava. (2018). Fish Tracking and Counting using Image Processing. , 1–4[10.1109/hnicem.2018.8666369]

10.Cyril Jay L. Chan, Ethan James A. Reyes, Noel B. Linsangan, Roben A. Juanatas. (2022). Real-time Detection of Aquarium Fish Species Using YOLOv4-tiny on Raspberry Pi 4. 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 1–6[10.1109/iicaiet55139.2022.9936790]

11.Krithika M. Pai, K. B. Ajitha Shenoy, M. M. Manohara Pai. (2022). A Computer Vision Based Behavioral Study and Fish Counting in a Controlled Environment. IEEE Access, 10, 87778–87786[10.1109/access.2022.3197887]

12.Junhui Wang, Meng Zhao, Li Zou, Yi Hu, Xuezhen Cheng, Xiaofeng Liu. (2019). Fish Tracking Based on Improved TLD Algorithm in Real-World Underwater Environment. Marine Technology Society Journal, 53(3), 80–89[10.4031/mtsj.53.3.8]

13.Universitas Dian Nuswantoro, Ricardus Pramunendar, Dwi Prabowo, Universitas Dian Nuswantoro, Dewi Pergiwati, Universitas Dian Nuswantoro, Yuslena Sari, Universitas Lambung Mangkurat, Pulung Andono, Universitas Dian Nuswantoro, Moch Soeleman, Universitas Dian Nuswantoro. (2020). New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique. International journal of intelligent engineering and systems, 13(4), 293–304[10.22266/ijies2020.0831.26]

14.Jianhao Yin, Junfeng Wu, Chunqi Gao, Zhongai Jiang. (2022). LIFRNet: A Novel Lightweight Individual Fish Recognition Method Based on Deformable Convolution and Edge Feature Learning. Agriculture, 12(12), 1972[10.3390/agriculture12121972]

15.Universitas Gadjah Mada, Ricardus Pramunendar, Sunu Wibirama, Universitas Gadjah Mada, Paulus Santosa, Universitas Gadjah Mada, Pulung Andono, Universitas Dian Nuswantoro, Moch Soeleman, Universitas Dian Nuswantoro. (2019). A Robust Image Enhancement Techniques for Underwater Fish Classification in Marine Environment. International journal of intelligent engineering and systems, 12(5), 116–129[10.22266/ijies2019.1031.12]

16.K Banno, H Kaland, AM Crescitelli, SA Tuene, GH Aas, LC Gansel. (2022). A novel approach for wild fish monitoring at aquaculture sites: wild fish presence analysis using computer vision. Aquaculture Environment Interactions, 14, 97–112[10.3354/aei00432]

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