International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels’ fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to \(90\%\). Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of \(99\%\). We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.

国际组织敦促保护我们的海洋及其生态系统，因为它们对人类具有不可估量的重要性。由于非法捕捞活动（通常称为 IUU 捕捞）对这些生态系统造成了无法弥补的损害，有关生物体正在推动发现和打击 IUU 捕捞行为。自动识别系统可以定位渔船的位置和轨迹。在这项研究中，我们解决了根据 GPS 位置数据定义的轨迹行为检测船只渔具的任务，这是一项防止 IUU 捕捞行为扩散的有用任务。我们提出了一个新的数据库，其中包括跨越 7 种不同渔具的轨迹，并在时间序列分析问题中对这些轨迹进行分析。我们利用在线签名验证领域的特征提取技术来对船舶轨迹进行建模，并以局部和全局特征集的形式提取相关信息。我们展示了如何基于这些特征集，使用常见的监督学习算法对不同渔具的船舶运动学进行有效分类，准确率高达\(90\%\)。此外，出于多个组织对底拖网捕捞对海洋生物多样性不利影响的担忧，我们提出了一个二元分类实验，我们能够以\(99\%\)的准确度区分这种渔具。我们还在消融研究中说明了数据可用性和执行渔具分类的采样周期等因素的相关性。与现有的工作相比，我们强调了这些因素，特别是使用分钟而不是小时的采样周期的重要性。