Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects while mitigating latency and data redundancy. Existing event-based clustering and feature tracking approaches for surveillance and object detection work well in the majority of cases, but fall short in a maritime environment. Our application of maritime vessel detection and tracking requires a process that can identify features and output a confidence score representing the likelihood that the feature was produced by a vessel, which may trigger a subsequent alert or activate a classification system. However, the maritime environment presents unique challenges such as the tendency of waves to produce the majority of events, demanding the majority of computational processing and producing false positive detections. By filtering redundant events and analyzing the movement of each event cluster, we can identify and track vessels while ignoring shorter lived and erratic features such as those produced by waves.
翻译:活动摄像机对于物体跟踪应用是理想的,因为它们能够捕捉快速移动的物体,同时减少悬浮和数据冗余。现有的基于事件的集群和特征跟踪监视和物体探测方法在多数情况下效果良好,但在海洋环境中却不尽人意。我们的海上船只探测和跟踪应用需要一个过程,能够识别特征并产生信任分数,表明该特征可能由船只生成,从而触发随后的警报或启动分类系统。然而,海洋环境提出了独特的挑战,例如波浪产生大多数事件的趋势,要求大多数计算处理和生成虚假的正面探测。通过过滤冗余事件和分析每个事件集群的移动,我们可以识别和跟踪船只,同时忽略短命和不稳定的特征,例如波浪产生的特征。