Successful marine spatial planning relies on understanding patterns of human use, with accurate, detailed, and up-to-date information about the spatial distribution of fishing effort. In commercial vessels, tracking systems like the vessel monitoring system (VMS) or the automatic identification system (AIS) have helped to maintain and enhance the biodiversity of areas by generating large sources of positional data that served for commercial marine spatial planning. However, there is no regulation regarding location systems such as VMS or AIS for marine recreational fishing boats. Obtaining spatial data on marine recreational fishing can be difficult and time-intensive given the widespread and variable nature of the fleet. Remote cameras and computer vision systems are increasingly used to overcome the cost limitations of these conventional methods. Here we show a novel high-resolution and low-cost tracking system based on photo time-lapses and state-of-the-art computer vision algorithms, including deep learning, to automatically classify and obtain precise trajectories of fishing and cruising boats in coastal areas. Our method contributes to the automatic surveillance of marine protected areas by providing an image-based tool for automatic, real-time monitoring. Our method also allows for determining the intensity and spatial-temporal distribution of recreational fishing effort, important to defining the sustainability of the activity and coastal areas. We finally discuss the opportunities and limitations of computer vision tools applied to marine recreational fisheries spatial planning.

成功的海洋空间规划依赖于对人类使用模式的理解，以及有关捕捞努力量空间分布的准确、详细和最新的信息。在商业船舶中，船舶监控系统 (VMS) 或自动识别系统 (AIS) 等跟踪系统通过生成用于商业海洋空间规划的大量位置数据源，有助于维护和增强区域的生物多样性。然而，对于海洋休闲渔船的定位系统（例如 VMS 或 AIS）尚无规定。鉴于船队分布广泛且变化多端，获取海洋休闲捕鱼的空间数据可能很困难且耗时。远程摄像机和计算机视觉系统越来越多地用于克服这些传统方法的成本限制。在这里，我们展示了一种基于照片延时和最先进的计算机视觉算法（包括深度学习）的新型高分辨率和低成本跟踪系统，可自动分类并获得沿海渔船和游轮的精确轨迹地区。我们的方法通过提供基于图像的自动实时监控工具，有助于海洋保护区的自动监控。我们的方法还可以确定休闲捕鱼活动的强度和时空分布，这对于确定活动和沿海地区的可持续性非常重要。最后，我们讨论了计算机视觉工具应用于海洋休闲渔业空间规划的机会和局限性。