  Existing deep multi-object tracking (MOT) approaches first learn a deep
representation to describe target objects and then associate detection results
by optimizing a linear assignment problem. Despite demonstrated successes, it
is challenging to discriminate target objects under mutual occlusion or to
reduce identity switches in crowded scenes. In this paper, we propose learning
deep conditional random field (CRF) networks, aiming to model the assignment
costs as unary potentials and the long-term dependencies among detection
results as pairwise potentials. Specifically, we use a bidirectional long
short-term memory (LSTM) network to encode the long-term dependencies. We pose
the CRF inference as a recurrent neural network learning process using the
standard gradient descent algorithm, where unary and pairwise potentials are
jointly optimized in an end-to-end manner. Extensive experimental results on
the challenging MOT datasets including MOT-2015 and MOT-2016, demonstrate that
our approach achieves the state of the art performances in comparison with
published works on both benchmarks.
