License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images

In this thesis, we propose a license plate recognition system and study the feasibility of using synthetic training samples to train convolutional neural networks for a practical application. First we develop a modular framework for synthetic license plate generation; to generate different license plate types (or other objects) only the first module needs to be adapted. The other modules apply variations to the training samples such as background, occlusions, camera perspective projection, object noise and camera acquisition noise, with the aim to achieve enough variation of the object that the trained networks will also recognize real objects of the same class. Then we design two convolutional neural networks of low-complexity for license plate detection and character recognition. Both are designed for simultaneous classification and localization by branching the networks into a classification and a regression branch and are trained end-to-end simultaneously over both branches, on only our synthetic training samples. To recognize real license plates, we design a pipeline for scale invariant license plate detection with a scale pyramid and a fully convolutional application of the license plate detection network in order to detect any number of license plates and of any scale in an image. Before character classification is applied, potential plate regions are un-skewed based on the detected plate location in order to achieve an as optimal representation of the characters as possible. The character classification is also performed with a fully convolutional sweep to simultaneously find all characters at once. Both the plate and the character stages apply a refinement classification where initial classifications are first centered and rescaled. We show that this simple, yet effective trick greatly improves the accuracy of our classifications, and at a small increase of complexity. To our knowledge, this trick has not been exploited before. To show the effectiveness of our system we first apply it on a dataset of photos of Italian license plates to evaluate the different stages of our system and which effect the classification thresholds have on the accuracy. We also find robust training parameters and thresholds that are reliable for classification without any need for calibration on a validation set of real annotated samples (which may not always be available) and achieve a balanced precision and recall on the set of Italian license plates, both in excess of 98%. Finally, to show that our system generalizes to new plate types, we compare our system to two reference system on a dataset of Taiwanese license plates. For this, we only modify the first module of the synthetic plate generation algorithm to produce Taiwanese license plates and adjust parameters regarding plate dimensions, then we train our networks and apply the classification pipeline, using the robust parameters, on the Taiwanese reference dataset. We achieve state-of-the-art performance on plate detection (99.86% precision and 99.1% recall), single character detection (99.6%) and full license reading (98.7%)

Object Segmentation And Recognition Using Gradient Based Descriptors And Shape Driven Fast Marching Methods

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada, aktif çevrit nesne bölütleyici yöntemlerle birlikte kullanılabilecek yeni bir şekil betimleme ve tanıma sistemi önerilmiştir. Önerilen sistem daha önce yapılan çalışmalar gibi aktif çevriti önceden tanımlı şekillerden birine zorlamak yerine, çevrit nesne sınırlarına yapışırken aynı zamanda şekil betimleme yapmayı amaçlamıştır. Aktif çevrit bölütleyici olarak Hızlı Yürüme (Fast Marching) algoritması kullanılmış, Hızlı Yürüme metodu için yeni bir hız işlevi tanımlanmıştır. Ayrıca çevriti nesne sınırlarından geçtiği sırada durdurmayı amaçlayan özgün yaklaşımlar önerilmiştir. Çalışmanın en önemli katkılarından birisi yeni ortaya atılan Gradyan Temelli Şekil Betimleyicisi (GTŞB) dir [1]. GTŞB, aktif çevrit bölütleyicilerin yapısına uygun, sınır tabanlı, hem ikili hem de gri-seviyeli görüntülerle rahatça kullanılabilecek başarılı bir şekil betimleyicidir. GTŞB nin araç plaka karakter veritabanı, MPEG-7 şekil veritabanı, Kimia şekil veritabanı gibi farklı şekil veritabanlarında elde ettiği başarılar diğer çok bilinen sınır tabanlı betimleyicilerle de karşılaştırılarak verilmiştir. Elde edilen sonuçlar GTŞB nin tüm veritabanlarında diğer yöntemlere göre daha başarılı olduğunu işaret etmektedir. Çalışmada geliştirilen bir diğer önemli yaklaşım da Hızlı Yürüme çevritinin nesne sınırına yaklaşırken örneklenerek şeklin birden fazla defa betimlenmesine olanak veren yeni sınıflandırıcı yapıdır. Bu yaklaşım nesne tanımayı bir denemede sonuçlandıran geleneksel yöntemlerin bu sınırlamasını aşarak aynı nesneyi birçok kez tanıma olanağı sunmaktadır. Bu tanıma sonuçlarının tümleştirilmesiyle tek tanımaya göre daha yüksek başarılar elde edildiği çalışmanın ilgili bölümlerinde başarıları karşılaştıran tablolar yardımıyla gösterilmektedir.In this thesis, a gradient based shape description and recognition methodology to use with active contour-based object segmentation systems has been proposed. The Fast Marching (FM) active contour evolving model is utilized for boundary segmentation. A new speed functional has been defined to use first and second order image intensity derivatives. A local front stopping algorithm has also been proposed to improve the boundary handling performance of the FM model. The most critical improvement of the thesis is defining a new shape descriptor called the Gradient Based Shape Descriptor (GBSD) [1]. GBSD is a new boundary-based shape descriptor that can operate on both binary and gray-scaled images. The recognition performance of GBSD is measured on a license plate character database, MPEG-7 Core Experiments shape data set and Kimia data Set. The success rates are compared with other well-known boundary-based shape descriptors and it is shown that GBSD achieves better recognition percentages. A new recognition approach that utilizes the progressive active contours while iterating towards the real object boundaries has been proposed. This approach provides the recognizer many trials for shape description; it removes the limitation of traditional recognition systems that have only one chance for shape classification. Test results shown in this study prove that the voted decision result among these iterated contours outperforms the ordinary individual shape recognizers.DoktoraPh

Training Load, Well-Being, and Readiness

The reprint represents the publication of the seven articles associated with the special issue "Training Load, Well-Being, and Readiness: Reducing Injury Risk and Improving Sports Performance". Within the reprint the readers can found evidence about training load monitoring in soccer, cyclists and regular gym exercises. We hope readers can find interesting the methodological approaches provided

LiDAR-Based Object Tracking and Shape Estimation

Umfeldwahrnehmung stellt eine Grundvoraussetzung für den sicheren und komfortablen Betrieb automatisierter Fahrzeuge dar. Insbesondere bewegte Verkehrsteilnehmer in der unmittelbaren Fahrzeugumgebung haben dabei große Auswirkungen auf die Wahl einer angemessenen Fahrstrategie. Dies macht ein System zur Objektwahrnehmung notwendig, welches eine robuste und präzise Zustandsschätzung der Fremdfahrzeugbewegung und -geometrie zur Verfügung stellt. Im Kontext des automatisierten Fahrens hat sich das Box-Geometriemodell über die Zeit als Quasistandard durchgesetzt. Allerdings stellt die Box aufgrund der ständig steigenden Anforderungen an Wahrnehmungssysteme inzwischen häufig eine unerwünscht grobe Approximation der tatsächlichen Geometrie anderer Verkehrsteilnehmer dar. Dies motiviert einen Übergang zu genaueren Formrepräsentationen. In der vorliegenden Arbeit wird daher ein probabilistisches Verfahren zur gleichzeitigen Schätzung von starrer Objektform und -bewegung mittels Messdaten eines LiDAR-Sensors vorgestellt. Der Vergleich dreier Freiform-Geometriemodelle mit verschiedenen Detaillierungsgraden (Polygonzug, Dreiecksnetz und Surfel Map) gegenüber dem einfachen Boxmodell zeigt, dass die Reduktion von Modellierungsfehlern in der Objektgeometrie eine robustere und präzisere Parameterschätzung von Objektzuständen ermöglicht. Darüber hinaus können automatisierte Fahrfunktionen, wie beispielsweise ein Park- oder Ausweichassistent, von einem genaueren Wissen über die Fremdobjektform profitieren. Es existieren zwei Einflussgrößen, welche die Auswahl einer angemessenen Formrepräsentation maßgeblich beeinflussen sollten: Beobachtbarkeit (Welchen Detaillierungsgrad lässt die Sensorspezifikation theoretisch zu?) und Modell-Adäquatheit (Wie gut bildet das gegebene Modell die tatsächlichen Beobachtungen ab?). Auf Basis dieser Einflussgrößen wird in der vorliegenden Arbeit eine Strategie zur Modellauswahl vorgestellt, die zur Laufzeit adaptiv das am besten geeignete Formmodell bestimmt. Während die Mehrzahl der Algorithmen zur LiDAR-basierten Objektverfolgung ausschließlich auf Punktmessungen zurückgreift, werden in der vorliegenden Arbeit zwei weitere Arten von Messungen vorgeschlagen: Information über den vermessenen Freiraum wird verwendet, um über Bereiche zu schlussfolgern, welche nicht von Objektgeometrie belegt sein können. Des Weiteren werden LiDAR-Intensitäten einbezogen, um markante Merkmale wie Nummernschilder und Retroreflektoren zu detektieren und über die Zeit zu verfolgen. Eine ausführliche Auswertung auf über 1,5 Stunden von aufgezeichneten Fremdfahrzeugtrajektorien im urbanen Bereich und auf der Autobahn zeigen, dass eine präzise Modellierung der Objektoberfläche die Bewegungsschätzung um bis zu 30%-40% verbessern kann. Darüber hinaus wird gezeigt, dass die vorgestellten Methoden konsistente und hochpräzise Rekonstruktionen von Objektgeometrien generieren können, welche die häufig signifikante Überapproximation durch das einfache Boxmodell vermeiden

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

The Squad Animated Short film

The squad is an animation project which has the goal of create a full animated short film going through all the processes that it has, since the idea’s conception to the final rendering, trying to discover all the intrinsic parts of a professional production pipeline(work flow). Apart of that, this project also tries to put all this knowledge in practice creating a full media piece

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field