  Multi-target tracking (MTT) is a classical signal processing task, where the
goal is to estimate the states of an unknown number of moving targets from
noisy sensor measurements. In this paper, we revisit MTT from a deep learning
perspective and propose a convolutional neural network (CNN) architecture to
tackle it. We represent the target states and sensor measurements as images and
recast the problem as an image-to-image prediction task. Then we train a fully
convolutional model at small tracking areas and transfer it to much larger
areas with numerous targets and sensors. This transfer learning approach
enables MTT at a large scale and is also theoretically supported by our novel
analysis that bounds the generalization error. In practice, the proposed
transferable CNN architecture outperforms random finite set filters on the MTT
task with 10 targets and transfers without re-training to a larger MTT task
with 250 targets with a 29% performance improvement.
