Vacant Parking Lot Information System Using Transfer Learning and IoT

Parking information systems have become very important, especially in metropolitan areas as they help to save time, effort and fuel when searching for parking. This paper offers a novel low-cost deep learning approach to easily implement vacancy detection at outdoor parking spaces with CCTV surveillance. The proposed method also addresses issues due to perspective distortion in CCTV images. The architecture consists of three classifiers for checking the availability of parking spaces. They were developed on the TensorFlow platform by re-training MobileNet (a pre-trained Convolutional Neural Network (CNN)) model using the transfer learning technique. A performance analysis showed 88% accuracy for vacancy detection. An end-to-end application model with Internet of Things (IoT) and an Android application is also presented. Users can interact with the cloud using their Android application to get real-time updates on parking space availability and the parking location. In the future, an autonomous car could use this system as a V2I (Vehicle to Infrastructure) application in deciding the nearest parking space

Detection of Empty/Occupied States of Parking Slots in Multicamera system using Mask R-CNN Classifier

A fast growth of vehicles in big cities has an impact of arising road loads and difficulty of finding empty parking spaces. One solution to cope with the problem is to develop a parking management system which can provide useful information of available parking spaces to the potential users. This paper discusses about a new multicamera arrangement and the function to evaluate the empty/occupied states of the parking slots, as an alternative solution to the existing single camera system, The system adopted Mask R-CNN for its classifier, because of its capability to provide the polygon outputs for its detected objects, compared with the existing bounding box outputs provided by other classifiers. The proposed function has optimized the available information from all cameras, by considering the relative position of each camera to the parking spaces, and also capable of overcoming occlusion problem occurs in some cameras, The experiment shows that the capability of overcoming the occlusion problem has been validated, and its performance to evaluate the empty/occupied states of the parking slots was better than the single camera system to a certain threshold

Deep Learning Based Parking Vacancy Detection for Smart Cities

Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services

Automatic Vision-Based Parking Slot Detection and Occupancy Classification

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.Comment: 39 pages, 8 figures, 9 table

Smart Parking System Using Color QR Code

In today’s world, parking area constitutes nearly most of traffic congestion is caused by vehicles cruising around their destination and looking for a place to park. Due to this reason many day-to-day activities are affected such as waste of time, fuel wastage, frustration to drivers, theft fear, pollution etc. These factors motivated to pave a new method for smart parking system. In this method the detection is reliable, even when tests are performed using images captured from a different viewpoint. It also provides to design a highly reliable & compatible image segmentation measures for parking slot identification system and a user key driven data base measures to detect the vehicle using theft alarm system