SISTEM INFORMASI PARKIR MENGGUNAKAN TEKNIK OBJECT TRACKING DAN OBJECT COUNTING

Fasilitas parkir merupakan hal yang sangat penting untuk para pengguna kendaraan, khususnya kendaraan beroda empat. Di sebagian besar negara, mobil adalah moda transportasi yang dominan, bahkan diperkirakan pada tahun 2050 mendatang benua Asia akan mengalami pertumbuhan penggunaan mobil pribadi sebesar 40%. Dalam memaksimalkan efisiensi penggunaan lahan parkir, diperlukan sistem informasi parkir yang memberikan informasi ketersediaan tempat parkir untuk mempermudah pengendara menggunakan fasilitas parkir. Namun demikian, permasalahan terkait hunian parkir masih sering terjadi, seperti dari tidak adanya informasi lahan parkir yang tersedia, lambatnya informasi ketersediaan lahan parkir dan minimnya informasi terkait hunian parkir. Ditambah lagi dengan peningkatan kepemilikan mobil, menyebabkan kurangnya area parkir mobil karena tidak seimbangnya antara ketersediaan parkir dan kebutuhan parkir. Berbagai sistem berbasis sensor maupun vision dirancang untuk mengatasi masalah tersebut, namun masih terdapat kekurangan dari kinerja sistem dalam hal akurasi dan kecepatan pemrosesan. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan sistem menggunakan model YOLOv8 dengan teknik object tracking serta object counting. Teknik object tracking digunakan untuk meningkatkan akurasi dan kestabilan deteksi dalam setiap frame khususnya dalam video. Kemudian object counting dimanfaatkan dengan membuat zona deteksi untuk meningkatkan keakuratan penghitungan ketersediaan lahan parkir. Informasi yang dihasilkan dari proses deteksi, tracking, dan counting disimpan pada database lokal dan ditampilkan pada aplikasi berbasis web sehingga membantu mengetahui informasi ketersediaan parkir. Pada penelitian ini didapatkan hasil bahwa sistem yang dirancang memiliki kinerja baik dan dapat menandingi sistem yang dibangun oleh peneliti lainnya dengan akurasi rata-rata sebesar 98,67%, latensi sekitar 20 milidetik, dan 50 frame per second. ; Parking facilities are very important for vehicle users, especially four-wheeled vehicles. In most countries, cars are the dominant mode of transportation, it is estimated that by 2050 the Asian continent will experience a growth in private car use of 40%. In maximizing the efficiency of the use of parking space, a parking information system is needed, prodividng information about the availability of parking spaces to make it easier for drivers to use parking facilities. However, problems related to parking occupancy still occur frequently, such as the unavailability of parking space information, the speed of parking space information retrieved, and the lack of information about parking occupancy. Moreover, the increase in car ownership causes a shortage of car parking areas due to an imbalance between parking availability and parking needs. Various sensor and vision-based systems are designed to overcome these problems, but there are still deficiencies in system performance in terms of accuracy and processing speed. To overcome these problems, this study proposes a system using the YOLOv8 model with object tracking and object counting techniques. Object tracking techniques are used to improve the accuracy and stability of detection in each frame, especially in video. Then object counting is utilized by creating detection zones to improve the accuracy of calculating parking space availability. The information generated from the detection, tracking and counting processes is stored in a local database and displayed on a web-based application that helps determine parking availability information. In this study, the results showed that the designed system had good performance and could compete with systems built by other researchers with an average accuracy of 98.67%, a latency of around 20 milliseconds, and 50 frames per second

MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV

The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP)

Finding parking in a busy city has been a major daily problem in today’s busy life. Researchers have proposed various parking spot detection systems to overcome the problem of spending a long time searching for a parking spot. These works include a wide variety of sensors to detect the presence of a vehicle in a parking spot. These approaches are expensive to implement and ineffective in extreme weather conditions in an outdoor parking environment. As a result, a cost-effective, dependable, and time-saving parking solution is much more desirable. In this thesis, we proposed and developed an image processing-based real-time parking-spot detection system using deep-learning algorithms. In this regard, we annotated the images using the Visual Geometry Group (VGG) annotator and preprocessed the dataset using the image contrast enhancement technique that attempts to solve the illumination changes in pictures captured in an open space, followed by training the model using the Mask-R-CNN (Region-Based Convolutional Neural Network) and Faster-RCNN algorithms. ROIs (Regions of interest) are used later to determine the vacancy status of each parking spot. Our experimental results demonstrate the effectiveness of our developed parking systems as we achieved a mean Average Precision (mAP) of 0.999 for the PKLot dataset and a mAP of 0.9758 for custom datasets. Furthermore, as part of the smart parking application, we developed an Android App that can be used by the end users. Our proposed intelligent parking system is scalable, cost-effective, and to the best of our knowledge, it offers higher parking spot detection accuracy than any other solutions in this domain

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots