Multiple Fish Tracking via Viterbi Data Association for Low-Frame-Rate Underwater Camera Systems †

Abstract-Non-extractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Low frame rate and variable illumination in the underwater environment, however, makes conventional tracking methods unreliable. In this paper, a robust multiple fish tracking system for low-framerate underwater stereo cameras is proposed. With the result of fish segmentation, a computationally efficient block-matching method is applied to perform successful stereo matching. A multiple-feature matching cost function is utilized to give a simple but effective metric for finding the temporal match of each target. Built upon reliable stereo matching, a multipletarget tracking algorithm via the Viterbi data association is developed to overcome the poor motion continuity of targets. Experimental results show that an accurate underwater live fish tracking result with stereo cameras is achieved

Change detection in combination with spatial models and its effectiveness on underwater scenarios

This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm

Video Analysis : Techniques for Semi-Supervised Video Object Instance Segmentation and Tracking-by-Detection in the Wild

This thesis consists of two major components. The first part is concerned with video object instance segmentation (VOS), which is the task of assigning per-pixel labels perframe of a video sequence to indicate foreground object instance membership, given the first frame ground truth mask. VOS has myriad applications, from video post-processing to action recognition, and is an active area of research. A novel end-to-end trainable, online algorithm utilizing a bilinear LSTM to learn long-term appearance models is presented. The bilinear LSTM is used to guide the learned CNN features, integrating temporal information and building more discriminative appearance features for specific objects during inference. The second part of this thesis examines computer vision's potential applications for performing automated ecological inference for endemic flat-fish populations. Specifically, it looks at the construction of a visual tracking dataset, NHFish, consisting of underwater beam trawl videos collected along the Newport Hydrographic Line of Oregon coast benthos and the application of automated methods for video analysis of the beam trawl videos

Applications of deep learning in fish habitat monitoring: A tutorial and survey

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue