Computer Vision Methods for Parking Spot Extraction from LiDAR Top-down Images
Hiltunen, Tiitus (2022)
2022
Tietotekniikan DI-ohjelma - Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2022-02-18
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202202041836
https://urn.fi/URN:NBN:fi:tuni-202202041836
Tiivistelmä
The goal of parking spot extraction is to find out the corner coordinates of a parking spot in pixel coordinates. If the image is georeferenced, where each pixel coordinate has a corresponding longitudinal and latitudinal coordinate, this information can be used to find out the coordinates of the parking spot in geographical coordinates. This opens up door for multiple applications such as speeding up the development of annotating parking garages. This annotation is important for example for autonomous vehicles to navigate inside the parking garage. Typically these georeferenced images are projected from a top-down view in relation to the parking spot.
Albeit parking spots being quite a simple feature in terms of their visual structure, parking spot extraction from top-down images is a complex and multifaceted problem. This is especially the case when using deep learning approaches and there is a lack of quantity and diversity of data samples, which makes it difficult to use advanced deep learning methods such as instance segmentation.
If the parking spot extractor model is not based on deep learning, some assumptions must be made about the visual nature of parking spots. Parking spots are typically rectangular, but parallelogram-shaped parking spots are not atypical either. In addition, parking spots can be in any rotation in the top-down image or inhabited by a car. They can also have visual cues symbolizing, for example, a parking spot reserved for families or people with physical disabilities.
The top-down images utilized in this work were based on point clouds, which were gathered using scanners inside parking garages. These point clouds and the top-down images generated from them produce their own set of problems for feature extraction, mainly the noisiness due to negligent scanning. Therefore the model has to be robust to be able to detect partially lost parking spots and parking spots that have only some parts of the lines visible in the top-down image.
The proposed method utilized histogram of oriented gradients features along with a logistic regression classifier for parking spot proposals, and a convolutional neural network called TilhiNet for proposal verification. The experiments show that the histogram of oriented gradient features work well and robustly with a small amount of sample data in the case of parking spot extraction from point cloud-based top-down images.
Albeit parking spots being quite a simple feature in terms of their visual structure, parking spot extraction from top-down images is a complex and multifaceted problem. This is especially the case when using deep learning approaches and there is a lack of quantity and diversity of data samples, which makes it difficult to use advanced deep learning methods such as instance segmentation.
If the parking spot extractor model is not based on deep learning, some assumptions must be made about the visual nature of parking spots. Parking spots are typically rectangular, but parallelogram-shaped parking spots are not atypical either. In addition, parking spots can be in any rotation in the top-down image or inhabited by a car. They can also have visual cues symbolizing, for example, a parking spot reserved for families or people with physical disabilities.
The top-down images utilized in this work were based on point clouds, which were gathered using scanners inside parking garages. These point clouds and the top-down images generated from them produce their own set of problems for feature extraction, mainly the noisiness due to negligent scanning. Therefore the model has to be robust to be able to detect partially lost parking spots and parking spots that have only some parts of the lines visible in the top-down image.
The proposed method utilized histogram of oriented gradients features along with a logistic regression classifier for parking spot proposals, and a convolutional neural network called TilhiNet for proposal verification. The experiments show that the histogram of oriented gradient features work well and robustly with a small amount of sample data in the case of parking spot extraction from point cloud-based top-down images.