A Vision-Based Method for Parking Space Surveillance and Parking Lot Management

In this paper, we develop a new vision-based parking space surveillance system for parking lot management. The system consists of three parts. Initially, a feature-based background model using edge and color characteristics is proposed, and foreground feature is extracted to determine whether the parking space is vacant or not. Secondary, to capture the pictures when a car has been completely into the parking space, we employ adjacent frame difference image to find the static state of the parking space. Finally, for the final decision, an adaptive thresholds updating method is proposed. After experiments on different parking lots, the proposed system has been proved to be effective and accurate.

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