  This paper presents a novel solution to automatically count vehicles in a
parking lot using images captured by smart cameras. Unlike most of the
literature on this task, which focuses on the analysis of single images, this
paper proposes the use of multiple visual sources to monitor a wider parking
area from different perspectives. The proposed multi-camera system is capable
of automatically estimate the number of cars present in the entire parking lot
directly on board the edge devices. It comprises an on-device deep
learning-based detector that locates and counts the vehicles from the captured
images and a decentralized geometric-based approach that can analyze the
inter-camera shared areas and merge the data acquired by all the devices. We
conduct the experimental evaluation on an extended version of the CNRPark-EXT
dataset, a collection of images taken from the parking lot on the campus of the
National Research Council (CNR) in Pisa, Italy. We show that our system is
robust and takes advantage of the redundant information deriving from the
different cameras, improving the overall performance without requiring any
extra geometrical information of the monitored scene.
