  Parking spaces are costly to build, parking payments are difficult to
enforce, and drivers waste an excessive amount of time searching for empty
lots. Accurate quantification would inform developers and municipalities in
space allocation and design, while real-time measurements would provide drivers
and parking enforcement with information that saves time and resources. In this
paper, we propose an accurate and real-time video system for future Internet of
Things (IoT) and smart cities applications. Using recent developments in deep
convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we
combine information across multiple image frames in a video sequence to remove
noise introduced by occlusions and detection failures. We demonstrate that our
system achieves higher accuracy than pure image-based instance segmentation,
and is comparable in performance to industry benchmark systems that utilize
more expensive sensors such as radar. Furthermore, our system shows significant
potential in its scalability to a city-wide scale and also in the richness of
its output that goes beyond traditional binary occupancy statistics.
