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

Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Certain Investigations on Vehicles Number Plate Identification using Top Hat Transform Algorithm and Optical Character Recognition

Investigation on vehicles number plate with top hat transforms is the method to recognize the characters on number plate utilizing the process like Image processing and OCR. The conception of this project is, first the image of the vehicle is to be captured. Next, the number plate of the vehicles is extracted from captured images using Top Hat transform algorithms. Conclusively, Optical Character Recognization recognizes the character presented in number plate. Additionally, the extracted data is stored in our database. This project can be implemented on various security zones like Parking Systems, Traffic Control areas, Toll gates, tracking of vehicles, etc. In the current scenario, the usage of vehicles increases day by day. Hence it's impossible to maintain the record manually for entire Vehicles. By expanding this system it becomes easy to sustain such rather records. In the majority of the nations, the extent of the number plate relies upon the aspect ratio. It can be figured by Width over Height. This work proposes the strategy for following Indian Number Plates of the vehicle. While contrasting other number plate extraction strategy this technique varies in such a path, in several strategies, they utilized just an area of a number plate for recognizing the character. However, in this method the entire vehicle can be included which first finds the particular zone of number plate then it executes character Recognition. Template matching technique where used in previous methods of number plate identification which one and only needs an area of a number plate. The disadvantages of previous techniques are it can only recognize already stored character in the templates and if there is more than one number plate, it is impossible to identify the sector of the number plate. Therefore to overcome such errors, we developed this algorithm which relatively gives better results while comparing with other methods. The absolute time taken for one execution is below 5 seconds

An Efficient Method for Number Plate Detection and Extraction Using White Pixel Detection (WPD) Method

Intelligent transport systems play an important role in supporting smart cities because of their promising applications in various areas, such as electronic toll collection, highway surveillance, urban logistics and traffic management. One of the key components of intelligent transport systems is vehicle license plate recognition, which enables the identification of each vehicle by recognizing the characters on its license plate through various image processing and computer vision techniques. Vehicle license plate recognition typically consists of smoothing image using median filter, White pixel detection (WPD), and number plate extraction. In this work an efficient White pixel detection method has been describing a license plates in various luminance conditions. Mostly we will focus on vehicle number plate detection along with the white pixel detection method we will use median filters and Line density filters to increase the detection accuracy for number plate. Subjective and objective quality assessment parameters will give us robustness of proposed work compared to state of License Plate Detection(LPD) techniques

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

Vehicle Speed Measurement and Number Plate Detection using Real Time Embedded System

A real time system is proposed to detect moving vehicles that violate the speed limit. A dedicated digital signal processing chip is used to exploit computationally inexpensive image-processing techniques over the video sequence captured from the fixed position video camera for estimating the speed of the moving vehicles. The moving vehicles are detected by analysing the binary image sequences that are constructed from the captured frames by employing the inter-frame difference or the background subtraction techniques. The detected moving vehicles are tracked to estimate their speeds.This project deals with the tracking and following of single object in a sequence of frames and the velocity of the object is determined. The proposed method varies from previous existing methods in tracking moving objects, velocity determination and number plate detection. From the binary image generated, the moving vehicle is tracked using image segmentation of the video frames. The segmentation process is done by using the thresholding and morphological operations on the video frames. The object is visualized and its centroid is calculated. The distance it moved between frame to frame is stored and using this velocity is calculated with the frame rate of video.The images of the speeding vehicles are further analysed to detect license plate image regions. The entire simulation is done in matlab and simulink simulation software. Keywords:morphological;thresholding;segmentation;centroi