  In this paper we present a robust tracker to solve the multiple object
tracking (MOT) problem, under the framework of tracking-by-detection. As the
first contribution, we innovatively combine single object tracking (SOT)
algorithms with multiple object tracking algorithms, and our results show that
SOT is a general way to strongly reduce the number of false negatives,
regardless of the quality of detection. Another contribution is that we show
with a deep learning based appearance model, it is easy to associate detections
of the same object efficiently and also with high accuracy. This appearance
model plays an important role in our MOT algorithm to correctly associate
detections into long trajectories, and also in our SOT algorithm to discover
new detections mistakenly missed by the detector. The deep neural network based
model ensures the robustness of our tracking algorithm, which can perform data
association in a wide variety of scenes. We ran comprehensive experiments on a
large-scale and challenging dataset, the MOT16 benchmark, and results showed
that our tracker achieved state-of-the-art performance based on both public and
private detections.
