A Video Processing and Data Retrieval Framework for Fish Population Monitoring

In this work we present a framework for fish population monitoring through the analysis of underwater videos. We specifically focus on the user information needs, and on the dynamic data extraction and retrieval mechanisms that support them. Sophisticated though a software tool may be, it is ultimately important that its interface satisfies users' actual needs and that users can easily focus on the specific data of interest. In the case of fish population monitoring, marine biologists have to interact with a system which not only provides information from a biological point of view, but also offers instruments to let them guide the video processing task for both video and algorithm selection. This paper aims at describing the system's underlying video processing and workflow low-level details, and their connection to the user interface for on-demand data retrieval by biologists

A video processing and data retrieval framework for fish population monitoring

htmlabstractIn this work we present a framework for fish population monitoring through the analysis of underwater videos. We specifically focus on the user information needs, and on the dynamic data extraction and retrieval mechanisms that support them. Sophisticated though a software tool may be, it is ultimately important that its interface satisfies users' actual needs and that users can easily focus on the specific data of interest. In the case of fish population monitoring, marine biologists have to interact with a system which not only provides information from a biological point of view, but also offers instruments to let them guide the video processing task for both video and algorithm selection. This paper aims at describing the system's underlying video processing and workflow low-level details, and their connection to the user interface for on-demand data retrieval by biologists

Extracting Statistically Significant Behaviour from Fish Tracking Data With and Without Large Dataset Cleaning

Extracting a statistically significant result from video of natural phenomenon can be difficult for two reasons: (i) there can be considerable natural variation in the observed behaviour and (ii) computer vision algorithms applied to natural phenomena may not perform correctly on a significant number of samples. This study presents one approach to clean a large noisy visual tracking dataset to allow extracting statistically sound results from the image data. In particular, analyses of 3.6 million underwater trajectories of a fish with the water temperature at the time of acquisition are presented. Although there are many false detections and incorrect trajectory assignments, by a combination of data binning and robust estimation methods, reliable evidence for an increase in fish speed as water temperature increases are demonstrated. Then, a method for data cleaning which removes outliers arising from false detections and incorrect trajectory assignments using a deep learning‐based clustering algorithm is proposed. The corresponding results show a rise in fish speed as temperature goes up. Several statistical tests applied to both cleaned and not‐cleaned data confirm that both results are statistically significant and show an increasing trend. However, the latter approach also generates a cleaner dataset suitable for other analysis

"How" and "what" matters: Sampling method affects biodiversity estimates of reef fishes

Understanding changes in biodiversity requires the implementation of monitoring programs encompassing different dimensions of biodiversity through varying sampling techniques. In this work, fish assemblages associated with the "outer" and "inner" sides of four marinas, two at the Canary Islands and two at southern Portugal, were investigated using three complementary sampling techniques: underwater visual censuses (UVCs), baited cameras (BCs), and fish traps (FTs). We firstly investigated the complementarity of these sampling methods to describe species composition. Then, we investigated differences in taxonomic (TD), phylogenetic (PD) and functional diversity (FD) between sides of the marinas according to each sampling method. Finally, we explored the applicability/reproducibility of each sampling technique to characterize fish assemblages according to these metrics of diversity. UVCs and BCs provided complementary information, in terms of the number and abundances of species, while FTs sampled a particular assemblage. Patterns of TD, PD, and FD between sides of the marinas varied depending on the sampling method. UVC was the most cost-efficient technique, in terms of personnel hours, and it is recommended for local studies. However, for large-scale studies, BCs are recommended, as it covers greater spatio-temporal scales by a lower cost. Our study highlights the need to implement complementary sampling techniques to monitor ecological change, at various dimensions of biodiversity. The results presented here will be useful for optimizing future monitoring programs.FCT-Foundation for Science and Technology [CCMAR/Multi/04326/2013]info:eu-repo/semantics/publishedVersio