  Multi-Person Tracking (MPT) is often addressed within the
detection-to-association paradigm. In such approaches, human detections are
first extracted in every frame and person trajectories are then recovered by a
procedure of data association (usually offline). However, their performances
usually degenerate in presence of detection errors, mutual interactions and
occlusions. In this paper, we present a deep learning based MPT approach that
learns instance-aware representations of tracked persons and robustly online
infers states of the tracked persons. Specifically, we design a multi-branch
neural network (MBN), which predicts the classification confidences and
locations of all targets by taking a batch of candidate regions as input. In
our MBN architecture, each branch (instance-subnet) corresponds to an
individual to be tracked and new branches can be dynamically created for
handling newly appearing persons. Then based on the output of MBN, we construct
a joint association matrix that represents meaningful states of tracked persons
(e.g., being tracked or disappearing from the scene) and solve it by using the
efficient Hungarian algorithm. Moreover, we allow the instance-subnets to be
updated during tracking by online mining hard examples, accounting to person
appearance variations over time. We comprehensively evaluate our framework on a
popular MPT benchmark, demonstrating its excellent performance in comparison
with recent online MPT methods.
