Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

Automatic Vehicle Detection and Identification using Visual Features

In recent decades, a vehicle has become the most popular transportation mechanism in the world. High accuracy and success rate are key factors in automatic vehicle detection and identification. As the most important label on vehicles, the license plate serves as a mean of public identification for them. However, it can be stolen and affixed to different vehicles by criminals to conceal their identities. Furthermore, in some cases, the plate numbers can be the same for two vehicles coming from different countries. In this thesis, we propose a new vehicle identification system that provides high degree of accuracy and success rates. The proposed system consists of four stages: license plate detection, license plate recognition, license plate province detection and vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and histogram of oriented gradients (HOG) as training dataset. To reach high accuracy in real-time application, a novel method is used to update the system. Meanwhile, via the proposed system, we can store the vehicles features and information in the database. Additionally, with the database, the procedure can automatically detect any discrepancy between license plate and vehicles

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

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

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system

A design of license plate recognition system using convolutional neural network

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy

MINHLP: Module to Identify New Hampshire License Plates

A license plate, referred to simply as a plate or vehicle registration plate, is a small plastic or metal plate attached to a motor vehicle for official identification purposes. Most governments require a registration plate to be attached to both the front and rear of a vehicle, although certain jurisdictions or vehicle types, such as motorcycles, require only one plate, which is usually attached to the rear of the vehicle. We present analysis of Automatic License Plate Recognition (ALPR) of New Hampshire (NH) plates using open source products. This thesis contains an implementation of a demonstrated model and analysis of the results. In this paper, OpenCV (computer vision library) and Tesseract (open source optical character reader) is presented as a core intelligent infrastructure. The thesis explains the mathematical principles and algorithms used for number plate detection, processes of proper characters segmentation, normalization and recognition. A description of the challenges involved in detecting and reading license plate in NH, previous studies done by others and the strategies adopted to solve them is also given

Vehicle license plate detection and recognition

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%. After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably. The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features. Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%. The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulIncludes bibliographical references (pages 67-73)

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges. It becomes difficult to retain features during occlusion especially in case of complete occlusion. In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions. This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique. The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature. Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF). The algorithm’s tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time. Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination. All tests have been conducted on the MATLAB platform. The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases