Multi-Object Tracking based Roadside Parking Behavior Recognition

Roadside parking spaces can alleviate the shortage of parking spaces, but there are some shortcomings to the charges for roadside parking. The popular charging methods at present mainly include manual charging, geomagnetic detection charging, meter charging, etc. These methods have certain limitations, such as high cost, difficult deployment, and low acceptance of people. To solve the shortcomings of roadside parking charges, this thesis proposes a scheme based on deep learning and image recognition. More specifically, the thesis proposes a scheme for detecting and tracking vehicles, recognizing license plates, recognizing vehicle parking behavior, and recording vehicle parking periods through the monocular camera to solve the problem of roadside parking charges. The scheme has the advantages of convenient deployment, low labor cost, high efficiency, and high accuracy. The main work of this thesis is as follows: 1. Based on the You Only Look Once (YOLO) algorithm, this thesis proposes a trapezoidal convolution algorithm to detect objects and improve the detection efficiency for the problem that the vehicle is far and small in the image. 2. Proposes a one-stage license plate recognition scheme based on YOLO, aiming to simplify the license plate recognition process. 3. Depending on the characteristics of the vehicle, this thesis proposes a feature extraction model of the vehicle, called the horizontal and vertical separation model, which use to combine with the deep Simple Online and Real-time Tracking (SORT) object tracking framework to track the vehicle and improve the tracking efficiency. 4. Uses a Long Short-Term Memory (LSTM) model to classify the behavior of the vehicle into three types: Park, leave, and no behavior. 5. Groups these modules together, and the engineering code is debugged a lot to realize a complete Roadside Parking Behavior Recognition (RPBR) system

Real Time Vehicle License Plate Recognition on Mobile Devices

Automatic license plate recognition is useful in many contexts such as parking control, law enforcement and vehicle background checking. The high cost and low portability of commercial systems makes them inaccessible to the majority of end users. However, current mobile devices now have processors and cameras that make image processing and recognition applications feasible on them. This thesis investigates high accuracy real-time license plate recognition on a smartphone, taking into account device limitations. It first explores how, using the minimal image processing and simple configurable heuristics based on plate geometry, license plates and their characters can be detected in an image. Then, using minimal training data, it shows that a character recognition package can achieve high levels of accuracy. This approach accurately recognized 99 percent of plates appearing in a test set of videos of vehicles with New Zealand license plates

System for automatic detection and classification of cars in traffic

Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video. Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer. Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps). Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed

Machine vision based smart parking system using Internet of Things

It is expected that in the next decade, majority of world population will be living in cities. Better public services and infrastructures in the city are needed to cope with the booming population. City vehicles that cruising for parking have indirectly causing traffic, making one harder to travel around the city. Thus, a smart parking system can certainly lays the foundation to build a smart city. This paper proposed a cost-effective IoT smart parking system to monitor city parking space and provide real-time parking information to drivers. Moreover, instead of the conventional approach that uses embedded sensors to detect vehicles in the parking area, camera image and machine vision technology are used to obtain the parking status. In the prototype, twenty outdoor parking lots are covered using a 5 megapixel camera connected to Raspberry Pi 3 installed at the 5th floor of the nearby building. Machine vision in this project that involved motion tracking and Canny edge detection are programmed in Python 2 using OpenCV technology. Corresponding data is uploaded to an IoT platform called Ubidots for possible monitoring activity. An Android mobile application is designed for user to download real-time data of parking information. This paper introduces a low cost smart parking system with the overall detection accuracy of 96.40%. Also, the mobile application allows users to alert other car owners for any emergency incidents and double parking blockage. The developed system can provide a platform for users to search for empty car parking with ease and reduce the traffic issues such as illegal double parking especially in the urban area