  We propose the first learning-based approach for detection and trajectory
estimation of fast moving objects. 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. Instead of
solving the complex deblatting problem jointly, we split the problem into
matting and deblurring and solve them separately. The proposed method first
detects all fast moving objects as a truncated distance function to the
trajectory. Subsequently, a matting and fitting network for each detected
object estimates the object trajectory and its blurred appearance without
background. For the sharp appearance estimation, we propose an energy
minimization based deblurring. The state-of-the-art methods are outperformed in
terms of 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.
