  We propose the first learning-based approach for fast moving objects
detection. Such objects are highly blurred and move over large distances within
one video frame. Fast moving objects are associated with a deblurring and
matting problem, also called deblatting. We show that the separation of
deblatting into consecutive matting and deblurring allows achieving real-time
performance, i.e. an order of magnitude speed-up, and thus enabling new classes
of application. The proposed method detects fast moving objects as a truncated
distance function to the trajectory by learning from synthetic data. For the
sharp appearance estimation and accurate trajectory estimation, we propose a
matting and fitting network that estimates the blurred appearance without
background, followed by an energy minimization based deblurring. The
state-of-the-art methods are outperformed in terms of recall, precision,
trajectory estimation, and sharp appearance reconstruction. Compared to other
methods, such as deblatting, the inference is of several orders of magnitude
faster and allows applications such as real-time fast moving object detection
and retrieval in large video collections.
