  Self-supervised multi-object trackers have tremendous potential as they
enable learning from raw domain-specific data. However, their re-identification
accuracy still falls short compared to their supervised counterparts. We
hypothesize that this drawback results from formulating self-supervised
objectives that are limited to single frames or frame pairs. Such formulations
do not capture sufficient visual appearance variations to facilitate learning
consistent re-identification features for autonomous driving when the frame
rate is low or object dynamics are high. In this work, we propose a training
objective that enables self-supervised learning of re-identification features
from multiple sequential frames by enforcing consistent association scores
across short and long timescales. We perform extensive evaluations
demonstrating that re-identification features trained from longer sequences
significantly reduce ID switches on standard autonomous driving datasets
compared to existing self-supervised learning methods, which are limited to
training on frame pairs. Using our proposed SubCo loss function, we set the new
state-of-the-art among self-supervised methods and even perform on par with
fully supervised learning methods.
