  The AI City Challenge was created with two goals in mind: (1) pushing the
boundaries of research and development in intelligent video analysis for
smarter cities use cases, and (2) assessing tasks where the level of
performance is enough to cause real-world adoption. Transportation is a segment
ripe for such adoption. The fifth AI City Challenge attracted 305 participating
teams across 38 countries, who leveraged city-scale real traffic data and
high-quality synthetic data to compete in five challenge tracks. Track 1
addressed video-based automatic vehicle counting, where the evaluation being
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. Track 5 was a new track addressing vehicle retrieval
using natural language descriptions. The evaluation system shows a general
leader board of all submitted results, and a public leader board of results
limited to the contest participation rules, where 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 is limited. Results show the
promise of AI in Smarter Transportation. State-of-the-art performance for some
tasks shows that these technologies are ready for adoption in real-world
systems.
