Vacant Parking Slot Recognition Method for Practical Autonomous Valet Parking System Using around View Image

Abstract

The parking assist system (PAS) provides information of parking slots around the vehicle. As the demand for an autonomous system is increasing, intelligent PAS has been developed to park the vehicle without the driver’s intervention. To locate parking slots, most existing methods detect slot markings on the ground using an around-view monitoring (AVM) image. There are many types of parking slots of different shapes in the real world. Due to this fact, these methods either limit their target types or use predefined slot information of different types to cover the types. However, the approach using predefined slot information cannot handle more complex cases where the slot markings are connected to other line markings and the angle between slot marking is slightly different from the predefined settings. To overcome this problem, we propose a method to detect parking slots of various shapes without predefined type information. The proposed method is the first to introduce a free junction type feature to represent the structure of parking slot junction. Since the parking slot has a modular or repeated junction pattern at both sides, junction pair consisting of one parking slot can be detected using the free junction type feature. In this process, the geometrically symmetric characteristic of the junction pair is crucial to find each junction pair. The entrance of parking slot is reconstructed according to the structure of junction pair. Then, the vacancy of the parking slot is determined by a support vector machine. The Kalman tracker is applied for each detected parking slot to ensure stability of the detection in consecutive frames. We evaluate the performance of the proposed method by using manually collected datasets, captured in different parking environments. The experimental results show that the proposed method successfully detects various types of parking slots without predefined slot type information in different environments.

CNN-Based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction

“…• It presents quantitative evaluation results using two large-scale public datasets and shows that the proposed method gives a state-of-the-art performance. For line detection, Hough transform [24], [25], Radon transform [26], [27], or random sample consensus (RANSAC) algorithm [28], [29], [30], [31] have been utilized. For line combination, K-means clustering [26], grouping based on predetermined distances and parallel and perpendicular properties [25], [31], [32], [33] have been used.…”

“…Once parking slots are detected by the line-based or junction-based methods, their occupancies are then classified. To this end, difference-of-Gaussians-based histogram with linear discriminant analysis (LDA) classifier [38], Canny edges with naïve Bayes classifier [32], color histogram with support vector machine (SVM) classifier [27], ultrasonic sensor-based occupancy grid [33], [36] have been exploited.…”

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”