  The AI City Challenge was created to accelerate intelligent video analysis
that helps make cities smarter and safer. Transportation is one of the largest
segments that can benefit from actionable insights derived from data captured
by sensors, where computer vision and deep learning have shown promise in
achieving large-scale practical deployment. The 4th annual edition of the AI
City Challenge has attracted 315 participating teams across 37 countries, who
leveraged city-scale real traffic data and high-quality synthetic data to
compete in four challenge tracks. Track 1 addressed video-based automatic
vehicle counting, where the evaluation is conducted on both algorithmic
effectiveness and computational efficiency. Track 2 addressed city-scale
vehicle re-identification with augmented synthetic data to substantially
increase the training set for the task. Track 3 addressed city-scale
multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly
detection. The evaluation system shows two leader boards, in which a general
leader board shows all submitted results, and a public leader board shows
results limited to our contest participation rules, that teams are not allowed
to use external data in their work. The public leader board shows results more
close to real-world situations where annotated data are limited. Our results
show promise that AI technology can enable smarter and safer transportation
systems.
