Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach

Abstract

Recent years have witnessed a growing interest in developing automatic parking systems in the field of intelligent vehicles. However, how to effectively and efficiently locating parking-slots using a vision-based system is still an unresolved issue. Even more seriously, there is no publicly available labeled benchmark dataset for tuning and testing parking-slot detection algorithms. In this paper, we attempt to fill the above-mentioned research gaps to some extent and our contributions are twofold. Firstly, to facilitate the study of vision-based parking-slot detection, a large-scale parking-slot image database is established. This database comprises 8600 surround-view images collected from typical indoor and outdoor parking sites. For each image in this database, the marking-points and parking-slots are carefully labeled. Such a database can serve as a benchmark to design and validate parking-slot detection algorithms. Secondly, a learning-based parking-slot detection approach, namely PSD L , is proposed. Using PSD L , given a surround-view image, the marking-points will be detected first and then the valid parking-slots can be inferred. The efficacy and efficiency of PSD L have been corroborated on our database. It is expected that PSD L can serve as a baseline when the other researchers develop more sophisticated methods.

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

“…In the past few years, various vision-based parking slot detection methods in the around view image have emerged. These approaches mainly could be subdivided into three categories: line-based methods [8,9,27,28], marking point-based methods [10,11,[19][20][21][22], and segmentation-based methods [24,25,29]. Hamada et al [27] used the Sobel filter and probabilistic Hough transform to obtain lines as the potential parking slot markers.…”

“…At last, various types of parking slots can be predicted based on the characteristics of junction candidates. Zhang et al [10] proposed a machine learning-based approach called PSD_L, which detected the marking-points using AdaBoost [31] framework first and then inferred complete parking-slots using detection results. To further improve the robustness of parking slot detection, Zhang et al [11] then proposed a DCNN-based approach called DeepPS, which is the first work using deep learning techniques to detect parking slots.…”

“…Therefore, the parking slot head and marking points detection is the first and most important step of the parking slot detection. PSD_L [10] and DeepPS [11] are two representative marking point-based methods, of which PSD_L uses the machine learning-based detection scheme to detect marking points, and DeepPS utilizes the DCNN-based object detection framework to detect marking points. Despite these two methods that can detect various parking slots effectively, they need a complicated rule-based scheme or a time consuming local image classification to match the paired marking points of the entrance line, resulting in cumbersome steps to infer the complete parking slot.…”

“…As the most important component of the PAS, the task of the object position designation is to detect a vacant parking slot accurately. The PAS can be divided into four categories based on the parking space detection method: free space-based [3][4][5][6][7], parking slot marking-based [8][9][10][11], interface-based [12][13][14], and infrastructure-based [15][16][17]. Compared with other methods, the parking slot marking-based approach can be applied in wider situations, since it does not depend on the existence of adjacent vehicles or extra communication equipment.…”

Around View Monitoring-Based Vacant Parking Space Detection and Analysis

“…However, the overdependence on corner features can result in an inability to construct parking spaces because corners cannot be detected in dim indoor environments. For leaning-based parking space detection, Li et al [17,18] proposed a machine learning approach in the field of parking space detection. They adopted boosting decision trees to train a marking-point detector to infer the parking space.…”