Computer Vision Methods for Marine Ecology

计算机视觉是一个应用研究领域，这意味着不同的应用领域以不同的方式塑造了它的发展；生态和环境监测很可能成为计算机视觉领域重大创新的下一个驱动力。因此，我将探索和定义计算机视觉在海洋生态学中的新角色；拟议的研究计划建立在我展示的专业知识和最近在水下图像增强，栖息地测绘和分析，动物检测和计数以及动物行为分析方面的贡献之上。我的总体愿景是支持在监测海洋生态核心维度（如丰度、生物多样性和动物行为）的方法上进行范式转变。更具体地说，该研究项目将为海洋生态学从耗时、资源密集型、小规模观测研究向可扩展、自动化、数据驱动的方法成功过渡做出重大贡献。在未来五年，我将着重实现以下三个短期目标：A.开发海洋物种丰富度和生物多样性的视觉量化自动化方法B.开发海洋动物行为的视觉检测和分析自动化方法C.开发高效和可持续的水下视觉数据管理自动化方法短期目标支持环境监测方法的拟议范式转变，这是我的长期重点。生物多样性和丰度目前是在有限的时空背景下人工估计的：目标A和B提供了当前协议急需的扩大规模。目标C解决了在长时间监测大规模生态系统时获得的大量数据的有效利用。从社会的角度来看，我的研究解决了对更准确、更可持续（从计算角度来看）和更全面的环境监测的迫切需求。由于成熟的硬件技术允许在远程环境中经济高效地部署和操作相机，长期视觉数据收集最近在生态和生物研究的许多关键领域变得普遍。然而，收集到的大量图像显然超出了人类的分析能力。计算机视觉可以有效地解决这个大数据问题，从而有助于监测和改善生态系统的健康，这与我们社会的福祉有着复杂的联系。我们希望这一贡献将为加拿大新环境政策的制定提供信息，从而使加拿大能够在促进和实施气候适应型全球经济方面发挥主导作用。

Computer Vision is an applied field of research, which means that various application domains have shaped its progress in distinct ways; ecology and environmental monitoring are likely to be the next drivers for major innovations in the computer vision field. I will thus explore and define new roles for computer vision in marine ecology; the proposed research program is built upon my demonstrated expertise and recent contributions to underwater image enhancement, habitat mapping and analysis, detection and counting of animals, and animal behaviour analysis. My overarching vision is to champion a paradigm shift in the methodology for monitoring core dimensions of marine ecology, such as abundance, biodiversity, and animal behaviour. More specifically, the research program will significantly contribute to a successful transition from time-consuming, resource-intensive, small-scale observational studies towards scalable, automatic, data-driven methods for marine ecology. During the next five years, I will focus on three short-term objectives, as follows: A. Develop automated methods for the visual quantification of marine species abundance and biodiversity B. Develop automated methods for the visual detection and analysis of marine animal behaviour C. Develop automated methods for efficient and sustainable underwater visual data management The short-term objectives support the proposed paradigm shift in environmental monitoring methodologies, which is my long-term focus. Biodiversity and abundance are currently estimated manually within limited spatiotemporal contexts: objectives A and B provide a much needed up-scaling of the current protocols. Objective C addresses the efficient use of the vast amount of data acquired when monitoring large-scale ecosystems over extended periods of time. From a societal standpoint, my research addresses the stringent need for more accurate, sustainable (from a computational viewpoint) and comprehensive environmental monitoring. Due to maturing hardware technologies allowing for cost-efficient deployment and operation of cameras in remote environments, long-term visual data collection has recently become prevalent in many key areas of ecological and biological research. However, the vast amount of collected imagery clearly exceeds human analysis capabilities. Computer vision can efficiently address this Big Data problem, and thus can contribute to monitoring and improving the health of ecosystems, which is intricately linked to the well-being of our society. We hope that this contribution will inform the development of new Canadian environmental policies, and thus will enable Canada to play a leading role in promoting and implementing a climate resilient global economy.