Parking guidance information (PGI) systems are used to provide information to
drivers about the nearest parking lots and the number of vacant parking slots.
Recently, vision-based solutions started to appear as a cost-effective
alternative to standard PGI systems based on hardware sensors mounted on each
parking slot. Vision-based systems provide information about parking occupancy
based on images taken by a camera that is recording a parking lot. However,
such systems are challenging to develop due to various possible viewpoints,
weather conditions, and object occlusions. Most notably, they require manual
labeling of parking slot locations in the input image which is sensitive to
camera angle change, replacement, or maintenance. In this paper, the algorithm
that performs Automatic Parking Slot Detection and Occupancy Classification
(APSD-OC) solely on input images is proposed. Automatic parking slot detection
is based on vehicle detections in a series of parking lot images upon which
clustering is applied in bird's eye view to detect parking slots. Once the
parking slots positions are determined in the input image, each detected
parking slot is classified as occupied or vacant using a specifically trained
ResNet34 deep classifier. The proposed approach is extensively evaluated on
well-known publicly available datasets (PKLot and CNRPark+EXT), showing high
efficiency in parking slot detection and robustness to the presence of illegal
parking or passing vehicles. Trained classifier achieves high accuracy in
parking slot occupancy classification.

该研究提出了一种完全基于图像输入的自动停车位检测和占用分类算法 (APSD-OC)，该算法通过在鸟瞰图上进行聚类来检测停车位，然后使用特定训练的 ResNet34 深度分类器对每个检测到的停车位进行占用或空置的分类。该方法在公开数据集 (PKLot 和 CNRPark+EXT) 上进行了广泛评估，显示出较高的停车位检测效率和对非法停车或经过车辆的稳健性。训练的分类器在停车位占用分类方面取得了较高的准确性。

基于视觉的自动停车位检测和占用分类

Automatic Vision-Based Parking Slot Detection and Occupancy  Classification

Vision based solutions for the localization of vehicles have become popular
recently. We employ an image retrieval based visual localization approach. The
database images are kept with GPS coordinates and the location of the retrieved
database image serves as an approximate position of the query image. We show
that localization can be performed via descriptors solely extracted from
semantically segmented images. It is reliable especially when the environment
is subjected to severe illumination and seasonal changes. Our experiments
reveal that the localization performance of a semantic descriptor can increase
up to the level of state-of-the-art RGB image based methods.

本研究使用图像检索为基础的视觉定位方法，通过从语义分割图像中提取出的描述符可在强烈光照和季节变化的环境下实现可靠的定位，实验结果表明，这种方法的定位性能可与最先进的基于 RGB 图像的方法相媲美。