  Urban traffic optimization using traffic cameras as sensors is driving the
need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
This work introduces CityFlow, a city-scale traffic camera dataset consisting
of more than 3 hours of synchronized HD videos from 40 cameras across 10
intersections, with the longest distance between two simultaneous cameras being
2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in
terms of spatial coverage and the number of cameras/videos in an urban
environment. The dataset contains more than 200K annotated bounding boxes
covering a wide range of scenes, viewing angles, vehicle models, and urban
traffic flow conditions. Camera geometry and calibration information are
provided to aid spatio-temporal analysis. In addition, a subset of the
benchmark is made available for the task of image-based vehicle
re-identification (ReID). We conducted an extensive experimental evaluation of
baselines/state-of-the-art approaches in MTMC tracking, multi-target
single-camera (MTSC) tracking, object detection, and image-based ReID on this
dataset, analyzing the impact of different network architectures, loss
functions, spatio-temporal models and their combinations on task effectiveness.
An evaluation server is launched with the release of our benchmark at the 2019
AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to
compare the performance of their newest techniques. We expect this dataset to
catalyze research in this field, propel the state-of-the-art forward, and lead
to deployed traffic optimization(s) in the real world.
