In this paper, a parking slot detection algorithm based on a bird's eye view is proposed. A density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and template matching algorithm are fused to detect parking slots in a bird's eye view. Progressive probabilistic Hough line detection and an improved DBSCAN clustering algorithm is developed to locate the sidelines of parking slots. Then, template matching is provided to locate and classify the "T shape" and "L shape" marking points more accurately. Finally, the marking points and sidelines of parking slots are integrated to complete the parking slot detection. The recall rate and precision rate of experimental results are 74.4% and 92.0%.