• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.170-178
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• 2019

This paper proposes a parking space detection method for autonomous parking by using the Around View Monitor (AVM) image and Light Detection and Ranging (LIDAR) sensor fusion. This method consists of removing obstacles except for the parking line, detecting the parking line, and template matching method to detect the parking space location information in the parking lot. In order to remove the obstacles, we correct and converge LIDAR information considering the distortion phenomenon in AVM image. Based on the assumption that the obstacles are removed, the line filter that reflects the thickness of the parking line and the improved radon transformation are applied to detect the parking line clearly. The parking space location information is detected by applying template matching with the modified parking space template and the detected parking lines are used to return location information of parking space. Finally, we propose a novel parking space detection system that returns relative distance and relative angle from the current vehicle to the parking space.

• Journal of Broadcast Engineering
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• v.27 no.7
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• pp.999-1010
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• 2022

The increase in vehicle purchases worldwide is having a very significant impact on the availability of parking spaces. In particular, since it is difficult to secure a parking space in an urban area, it may be of great help to the driver to check vehicle parking information in advance. However, the current parking lot information is still operated semi-manually, such as notifications. Therefore, in this study, we propose a system for detecting a parking space using a relatively simple image processing method based on an image taken from the sky and evaluate its performance. The proposed method first converts the captured RGB image into a black-and-white binary image. This is to simplify the calculation for detection using discrete information. Next, a morphological operation is applied to increase the clarity of the binary image, and a template mask in the form of a bounding box indicating a parking space is applied to check the parking state. Twelve image samples and 2181 total of test, were used for the experiment, and a threshold of 40% was used to detect each parking space. The experimental results showed that information on the availability of parking spaces for parking users was provided with an accuracy of 95%. Although the number of experimental images is somewhat insufficient to address the generality of accuracy, it is possible to confirm the possibility of parking space detection with a simple image processing method.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10C
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• pp.629-634
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• 2011

Empty space in the parking lot of the parking space to analyze the effective use of technology and transportation may be useful in the jungle, However, conventional analytical methods impractical parking space or need a fast processing speed. In this paper, real-time parking, so parking monitoring methods for detection is proposed. Gray-level images using the proposed method to determine whether the parking and the parking space was used to analyze. To verify the performance of the proposed method in an outdoor parking lot 129 video capture and analysis of experimental results in 98.5% of the parking space, parking space, the success of the proposed method was proved to be effective in the analysis.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.31-40
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• 2022

YOLOv4 can be used for detecting parking vehicles in order to check a vehicle in out-door parking space. YOLOv4 has 9 anchor boxes in each of 13x13 grid cells for detecting a bounding box of object. Because anchor boxes are allocated based on each cell, there can be existed small observational error for detecting real objects due to the distance between neighboring cells. In this paper, we proposed YOLOv4 grid cell shift algorithm for improving the out-door parking vehicle detection accuracy. In order to get more chance for trying to object detection by reducing the errors between anchor boxes and real objects, grid cells over image can be shifted to vertical, horizontal or diagonal directions after YOLOv4 basic detection process. The experimental results show that a combined algorithm of a custom trained YOLOv4 and a cell shift algorithm has 96.6% detection accuracy compare to 94.6% of a custom trained YOLOv4 only for out door parking vehicle images.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.9-21
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• 2022

In this paper, we developed a 100% detection system for entering and leaving vehicles by improving the detection rate of existing detection cameras based on the LiDAR sensor, which is one of the core technologies of the 4th industrial revolution. Since the currently operating parking lot depends only on the recognition rate of the license plate number of about 98%, there are various problems such as inconsistency in the entry/exit count, inability to make a reservation in advance due to inaccurate information provision, and inconsistency in real-time parking information. Parking status information should be managed with 100% accuracy, and for this, we built a parking lot entrance/exit detection system using LIDAR. When a parking system is developed by applying the LIDAR sensor, which is mainly used to detect vehicles and objects in autonomous vehicles, it is possible to improve the accuracy of vehicle entry/exit information and the reliability of the entry/exit count with the detected sensing information. The resolution of LIDAR was guaranteed to be 100%, and it was possible to implement so that the sum of entering (+) and exiting (-) vehicles in the parking lot was 0. As a result of testing with 3,000 actual parking lot entrances and exits, the accuracy of entering and exiting parking vehicles was 100%.

• Smart Media Journal
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• v.13 no.1
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.147-153
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• 2015

We propose a detecting method for a car accident in parking lots. The proposed method consists of 3 parts : car detection, car tracking, and accident detection. In the car detection part, we detect the car using the pixel based foreground extraction method and the motion map. From the result of the car detection, the moving car is tracked. In the accident detection part, we set the accident detecting region in front of car, and then the car accident is detected using the difference of the motion. Experimental results show that the proposed method effectively detects the car accident in the parking lots.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.708-713
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• 2004

A method of individual vehicle detection using gray scale image acquired from a high position is proposed for guidance of incoming vehicles to vacant cells in a parking lot and other similar purposes. With the proposed method, each image region corresponding to a cell is fragmented according to density(gray level), and the distribution of segment area is analyzed to decide if a vehicle is present. The proposed method was tested on an actual outdoor parking lot during 2 days with different weather conditions from sunrise through sunset.