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This paper proposes an approach to detect moving objects in Wide Area MotionImagery (WAMI), in which the objects are both small and well separated.Identifying the objects only using foreground appearance is difficult since a$100-$pixel vehicle is hard to distinguish from objects comprising thebackground. Our approach is based on background subtraction as an efficient andunsupervised method that is able to output the shape of objects. In order toreliably detect low contrast and small objects, we configure the backgroundsubtraction to extract foreground regions that might be objects of interest.While this dramatically increases the number of false alarms, a ConvolutionalNeural Network (CNN) considering both spatial and temporal information is thentrained to reject the false alarms. In areas with heavy traffic, the backgroundsubtraction yields merged detections. To reduce the complexity of multi-targettracker needed, we train another CNN to predict the positions of multiplemoving objects in an area. Our approach shows competitive detection performanceon smaller objects relative to the state-of-the-art. We adopt a GM-PHD filterto associate detections over time and analyse the resulting performance.

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)