Helicopter Video Dataset for Detecting and Tracking

Abstract

Labeled real world data is often difficult to obtain and especially scarce in Navy-related domains. Currently, relevant annotated data that does exist is frequently limited to large or medium sized bounding boxes, making it difficult to train computer vision algorithms to recognize smaller objects of interest. In this work, we present a naval-specific video dataset of helicopter operations performed at sea. This dataset contains videos from multiple camera sensors to incorporate variations in lens distortions and camera noise. It consists of videos ranging from one to three minutes each recorded during Littoral Combat Ship (LCS) exercises off the California coast in the fall and winter. Special consideration was taken to emphasize small instances of helicopters relative to the field of view and therefore provides a more even ratio of small-, medium-, and large-sized bounding boxes for training more robust detectors and trackers. Following the conventions of the field, we define small, medium, and large objects as objects with bounding boxes sized: less than 32, 32-96, and greater than 96 pixels squared respectively. We benchmark these videos on object detection with special consideration given to small-object mean average precision.