A vision-based algorithm for parking lot utilization evaluation using conditional random fields

Abstract

In this paper, we present an algorithm for estimating the occupancy of individual parking spaces. Our method is based on a computer analysis of images obtained by a camera system monitoring the activities on a parking lot. The proposed method extensively uses a priori information about the parking lot layout and the general shape of well-parked cars, which is incorporated in a simplified probabilistic car model. Discriminative features are extracted from a normalized image of every parking space, the relevance of these gradient-based features is prioritized via a selective flow, and furthermore, their spatial relationship is revealed through an undirected graphical model. We strive to avoid the training phase to reduce the time required to bring the system into a fully operational state. The reliability of the here devised approach is evaluated on the set of video sequences captured during different phases of a day and the results are compared against the ground truth data. © 2013 Springer-Verlag.