A simple approach to count and track underwater fishes from videos

Fish are a critical component of marine biology; therefore, the accurate identification and counting of fish are essential for the objective monitoring and assessment of marine biological resources. High‐frequency adaptive resolution imaging sonar (ARIS) is widely used for underwater object detection and imaging, and it quickly obtains close‐up video of free‐swimming fish in high‐turbidity water environments. Nonetheless, processing the massive data output using imaging sonars remains a major challenge. Here, the authors developed an automatic image‐processing programme that fuses K‐nearest neighbour background subtraction with DeepSort target tracking to automatically track and count fish. The automatic programme was evaluated using four test data sets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free‐swimming fish targets with an accuracy index of 73% and a completeness index of 70%. Under appropriate conditions, this approach could replace time‐consuming semi‐automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real‐time data processing.

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“…Compared with optical video fish extraction, the accuracy of this experiment was slightly lower (Sharif et al, 2015).…”

Section: Fish Population and Accuracy Assessmentmentioning

confidence: 89%

Identification and counting of fish targets using adaptive resolution imaging sonar

Shen

Peng

Zhang

2023

Journal of Fish Biology

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“…However, the experiment was carried out under laboratory conditions. Sharif, Galip, Guler, and Uyaver (2015) used Kalman filter and Hungarian method to track the fish center to estimate the fish abundance in a crowded scene in consecutive frames. Fish was estimated by the number of trajectories.…”

Section: Counting Methods Based On Computer Vision Technologymentioning

confidence: 99%

Automatic counting methods in aquaculture: A review

Miao

Peng

et al. 2020

J World Aquaculture Soc

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Object counting in aquaculture is an important task, and has been widely applied in fish population estimation, estimation of lobster abundance and scallop stocks, and so forth. However, underwater object counting is challenging for biologists and marine scientists because of the diversity of backgrounds of the lake or ocean, the uncertainty of the object motion, and the occlusion between objects. With the rapid development of sensor, computer vision, and acoustic technologies, advanced and efficient counting methods are available in aquaculture. We reviewed underwater object counting methods in aquaculture, provided a survey including more than 50 articles in the recent 10 years, and analyzed the pros and cons of the counting methods and the applicable scenarios of those methods. Finally, the major challenges and future trends of underwater object counting in aquaculture are discussed.

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“…Galip et al [7] used Hungarian method [2], [174]- [176] and Kalman filter [177] to get trajectories of movers from their own laser scanned dataset. But detection of movers was done based on various thresholds.…”

Section: B Qualitative Description Of Algorithmsmentioning

confidence: 99%

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

Self Cite

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Privacy of people is a key factor in surveillance systems. Video camera brings us welloff color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

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A simple approach to count and track underwater fishes from videos

Cited by 19 publications

References 15 publications

Identification and counting of fish targets using adaptive resolution imaging sonar

Identification and counting of fish targets using adaptive resolution imaging sonar

Automatic counting methods in aquaculture: A review

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

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