  This paper proposes an approach to detect moving objects in Wide Area Motion
Imagery (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 the
background. Our approach is based on background subtraction as an efficient and
unsupervised method that is able to output the shape of objects. In order to
reliably detect low contrast and small objects, we configure the background
subtraction to extract foreground regions that might be objects of interest.
While this dramatically increases the number of false alarms, a Convolutional
Neural Network (CNN) considering both spatial and temporal information is then
trained to reject the false alarms. In areas with heavy traffic, the background
subtraction yields merged detections. To reduce the complexity of multi-target
tracker needed, we train another CNN to predict the positions of multiple
moving objects in an area. Our approach shows competitive detection performance
on smaller objects relative to the state-of-the-art. We adopt a GM-PHD filter
to associate detections over time and analyse the resulting performance.
