  Object-level data association is central to robotic applications such as
tracking-by-detection and object-level simultaneous localization and mapping.
While current learned visual data association methods outperform hand-crafted
algorithms, many rely on large collections of domain-specific training examples
that can be difficult to obtain without prior knowledge. Additionally, such
methods often remain fixed during inference-time and do not harness observed
information to better their performance. We propose a self-supervised method
for incrementally refining visual descriptors to improve performance in the
task of object-level visual data association. Our method optimizes deep
descriptor generators online, by continuously training a widely available image
classification network pre-trained with domain-independent data. We show that
earlier layers in the network outperform later-stage layers for the data
association task while also allowing for a 94% reduction in the number of
parameters, enabling the online optimization. We show that self-labelling
challenging triplets--choosing positive examples separated by large temporal
distances and negative examples close in the descriptor space--improves the
quality of the learned descriptors for the multi-object tracking task. Finally,
we demonstrate that our approach surpasses other visual data-association
methods applied to a tracking-by-detection task, and show that it provides
better performance-gains when compared to other methods that attempt to adapt
to observed information.

Computer Vision and Pattern Recognition;Robotics