Computer Vision / Video Analytics

AI Can Detect Open Parking Spaces

With as many as 2 billion parking spaces in the United States, finding an open spot in a major city can be complicated. To help city planners and drivers more efficiently manage and find open spaces, MIT researchers developed a deep learning-based system that can automatically detect open spots from a video feed.   

“Parking spaces are costly to build, parking payments are difficult to enforce, and drivers waste an excessive amount of time searching for empty lots,” the researchers stated in their paper. “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.”

Using NVIDIA GeForce GTX 1080Ti GPUs, with the cuDNN-accelerated TensorFlow deep learning framework, the team trained a convolutional neural network on millions of images and videos from several datasets, including the COCO dataset, to detect vehicles and their boundaries.  Once trained, the system can accurately detect open parking spaces.

For real-time processing, the algorithm also relies on the NVIDIA GeForce GTX 1080Ti GPUs.

This image shows the result of the instance segmentation algorithm used in this study, which detects object classes of interests, localizes them with a bounding box, and also provides a pixel-level localization or mask of identified objects.

“Unlike space-based methods that require labeling and training for every distinct parking facility, we only need to mark out parking lot boundaries and surrounding road areas once to configure our system for a new parking facility,” the researchers said. “Labelling was only required to validate our results.”

The system can also filter a large number of characterizing features including vehicle type, color, whether it is a conventional taxi or a delivery truck, hour-by-hour utilization metrics, past trends, and historical data about cars that enter a lot.

Accuracy results from validation datasets

In terms of accuracy, the system performs better than pure image-based methods. The system is also comparable in performance with advanced commercial systems that rely on more expensive sensors.

“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,” the researchers stated.

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