  The majority of existing solutions to the Multi-Target Tracking (MTT) problem
do not combine cues in a coherent end-to-end fashion over a long period of
time. However, we present an online method that encodes long-term temporal
dependencies across multiple cues. One key challenge of tracking methods is to
accurately track occluded targets or those which share similar appearance
properties with surrounding objects. To address this challenge, we present a
structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple
cues over a temporal window. We are able to correct many data association
errors and recover observations from an occluded state. We demonstrate the
robustness of our data-driven approach by tracking multiple targets using their
appearance, motion, and even interactions. Our method outperforms previous
works on multiple publicly available datasets including the challenging MOT
benchmark.
