  Existing Multiple Object Tracking (MOT) methods design complex architectures
for better tracking performance. However, without a proper organization of
input information, they still fail to perform tracking robustly and suffer from
frequent identity switches. In this paper, we propose two novel methods
together with a simple online Message Passing Network (MPN) to address these
limitations. First, we explore different integration methods for the graph node
and edge embeddings and put forward a new IoU (Intersection over Union) guided
function, which improves long term tracking and handles identity switches.
Second, we introduce a hierarchical sampling strategy to construct sparser
graphs which allows to focus the training on more difficult samples.
Experimental results demonstrate that a simple online MPN with these two
contributions can perform better than many state-of-the-art methods. In
addition, our association method generalizes well and can also improve the
results of private detection based methods.
