Abstract
We consider the task of classifying trajectories of boat activities as a proxy for assessing maritime threats. Previous approaches have considered entropy-based metrics for clustering boat activity into three broad categories: random walk, following, and chasing. Here, we comprehensively assess the accuracy of neural network-based approaches as alternatives to entropy-based clustering. We train four neural network models and compare them to shallow learning using synthetic data. We also investigate the accuracy of models as time steps increase and with and without rotated data. To improve test-time robustness, we normalize trajectories and perform rotation-based data augmentation. Our results show that deep networks can achieve a test-set accuracy of up to 100% on a full trajectory, with graceful degradation as the number of time steps decreases, outperforming entropy-based clustering.
Abstract (translated)
我们将对船只活动轨迹进行分类的任务视为评估海上威胁的一种代理方式。之前的处理方法使用基于熵的度量将船只活动聚类为三大类别：随机游走、跟随和追逐。在这里，我们全面评估了基于神经网络的方法作为基于熵的聚类替代方案的准确性。我们训练了四个神经网络模型，并通过合成数据与浅层学习进行了比较。我们也研究了随着时间步长增加以及有无旋转数据的情况下模型的准确性。为了提高测试时的鲁棒性，我们对轨迹进行归一化并执行基于旋转的数据增强。我们的结果显示，在完整轨迹上深度网络可以达到高达100%的测试集准确率，并且当时间步骤数减少时性能逐渐降低，优于基于熵的聚类方法。
URL
https://arxiv.org/abs/2410.20054
