  As Computer Vision technologies become more mature for intelligent
transportation applications, it is time to ask how efficient and scalable they
are for large-scale and real-time deployment. Among these technologies is
Vehicle Re-Identification which is one of the key elements in city-scale
vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id
mostly focus on improving the accuracy on existing re-id benchmarks and often
ignore computational complexity. To balance the demands of accuracy and
computational efficiency, in this work we propose a simple yet effective hybrid
solution empowered by self-supervised training which only uses a single network
during inference time and is free of intricate and computation-demanding add-on
modules often seen in state-of-the-art approaches. Through extensive
experiments, we show our approach, termed Self-Supervised and Boosted VEhicle
Re-Identification (SSBVER), is on par with state-of-the-art alternatives in
terms of accuracy without introducing any additional overhead during
deployment. Additionally we show that our approach, generalizes to different
backbone architectures which facilitates various resource constraints and
consistently results in a significant accuracy boost.
