Effective moving cast shadow detection for monocular color image sequences

For an accurate scene analysis in monocular image sequences, a robust segmentation of a moving object from the static background is generally required. However, the existence of moving cast shadow may lead to an inaccurate object segmentation, and as a result, lead to further erroneous scene analysis. An effective detection of moving cast shadow in monocular color image sequences is developed. Firstly, by realizing the various characteristics of shadow in luminance, chrominance, and gradient density, an indicator, called shadow confidence score, of the probability of the region classified as cast shadow is calculated. Secondly the canny edge detector is employed to detect edge pixels in the detected region. These pixels are then bounded by their convex hull, which estimates the position of the object. Lastly, by analyzing the shadow confidence score and the bounding hull, the cast shadow is identified as those regions outside the bounding hull and with high shadow confidence score. A number of typical outdoor scenes are evaluated and it is shown that our method can effectively detect the associated cast shadow from the object of interest.published_or_final_versio

Evaluation of transport events with the use of big data, artificial intelligence and augmented reality techniques

The phenomenon of "smart cities" generalizes the use of Information and Communication Technologies. The generation and use of data to manage mobility is a challenge that many cities are betting on and investing in. Through the Internet of all things (IoT) and the use of sensors and mechanisms for capturing information, the number of data analysis tools such as Big Data, Artificial Intelligence (AI), and Augmented Reality (AR) has increased. With the constant use of assisted process learning (Machine Learning), it’s possible to improve event interpretation through the customization of learning protocols. Repetitively trained software can identify relevant events and report changes in critical scenarios that can trigger a series of protocols. The use of artificial intelligence techniques makes it possible to automate monotonous processes and improve transport management. This article analyzes different technologies used to generate transport information and data validation. It is intended to experiment with the use of technologies in the detection of relevant facts, changes of state, and identification of events. It also measures the reliability level when detecting events, and studies the implementation of possible solutions into the transport management system, in order to assist in decision making processes.Postprint (published version

Histogram tabanlı algoritmalarla sanal giriş birimi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Gerçek zamanlı video kaynağından alınan görüntü çerçeveleri analiz edilerek insan ve objelerin hareketlerinin analiz edilmesi, sağlık sektörü başta olmak üzere, endüstri ve eğlence sektöründe yaygın bir şekilde kullanılmaktadır. Kullanılan bu sistemler oldukça pahalı olmalarından dolayı yaygın değildir. Bilgisayar yazılım ve donanımındaki hızlı gelişme ve beraberinde düşen fiyatlar, bu konudaki çalışmaları artırmıştır.Bu tez çalışmasında histogram tabanlı algoritmalarla bir sanal giriş birimi tasarlanmış ve gerçekleştirilmiştir. Gerçek zamanlı video kaynağından görüntü çerçeveleri alınarak, bu görüntülerden hareketli nesneler belirlenmiştir. Bu hareketlerden insan vücudu hareketlerine benzeyen hareketleri seçip, 15 farklı insan üst vücut pozisyonlarını Windows işletim sistemi ve Windows işletim sistemi altında çalışan programların kontrolü amaçlı kullanımı gerçekleştirilmiştir.Gerçekleştirilen sanal giriş birimi standart bilgisayar giriş birimlerinin birçok fonksiyonlarını yerine getirmektedir.Analysis of the movement of people and objects from real time image frames which is taken from video source is widely used especially in the health sector, industry and entertainment sector. These systems are not widespread because of being very expensive. Computer software and hardware development and with the fast falling prices, increased efforts in this regard.In this thesis, virtual input unit is designed and implemented with histogram based algorithms. Moving objects are determined from real time image frames which are received from the video source. Then the movement is selected which is similar to human body movements. 15 different human upper body positions are implemented to control programs which are running in the Windows operating system and Windows operating system based software?s.Implemented system performs many functions of the standard computer input unit

Image based human body rendering via regression & MRF energy minimization

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method

A mathematical model for computerized car crash detection using computer vision techniques

My proposed approach to the automatic detection of traffic accidents in a signalized intersection is presented here. In this method, a digital camera is strategically placed to view the entire intersection. The images are captured, processed and analyzed for the presence of vehicles and pedestrians in the proposed detection zones. Those images are further processed to detect if an accident has occurred; The mathematical model presented is a Poisson distribution that predicts the number of accidents in an intersection per week, which can be used as approximations for modeling the crash process. We believe that the crash process can be modeled by using a two-state method, which implies that the intersection is in one of two states: clear (no accident) or obstructed (accident). We can then incorporate a rule-based AI system, which will help us in identifying that a crash has taken or will possibly take place; We have modeled the intersection as a service facility, which processes vehicles in a relatively small amount of time. A traffic accident is then perceived as an interruption of that service

Image-based traffic monitoring system.

Lau Wai Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 63-65).Abstracts in English and Chinese.abstract --- p.I摘要 --- p.IIacknowledgement --- p.IIItable of contents --- p.IVlist of figures --- p.VIChapter CHAPTER 1 --- introduction --- p.1Chapter CHAPTER 2 --- literature review --- p.4Chapter 2.1 --- Traffic data collection methods --- p.4Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6Chapter 2.2.1 --- Vehicle tracking approaches --- p.7Chapter 2.2.2 --- Image processing techniques --- p.10Chapter CHAPTER 3 --- methodology --- p.15Chapter 3.1 --- Solution Concept --- p.16Chapter 3.2 --- System Framework --- p.18Chapter 3.2.1 --- Edge Detection Module --- p.20Chapter 3.2.2 --- Background Update Module --- p.22Chapter 3.2.3 --- Feature Extraction Modules --- p.25Chapter CHAPTER 4 --- experiments and evaluation --- p.41Chapter 4.1 --- Setup and Data Collection --- p.41Chapter 4.2 --- Evaluation Criteria --- p.42Chapter 4.3 --- Experimental Results --- p.44Chapter 4.3.1 --- Comparing overall accuracies --- p.44Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48Chapter 4.3.4 --- Comparing day and night conditions --- p.50Chapter 4.3.5 --- Testing on time-series of images --- p.52Chapter CHAPTER 5 --- analysis --- p.54Chapter 5.1 --- Strengths and Weaknesses --- p.54Chapter 5.1.1 --- Sobel Edge Histogram --- p.54Chapter 5.1.2 --- Horizontal Line Detection --- p.55Chapter 5.1.3 --- Block Detection --- p.56Chapter 5.1.4 --- Combined Learning --- p.57Chapter 5.1.5 --- Overall Framework --- p.58Chapter 5.2 --- Future Research --- p.59Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60Chapter 5.2.3 --- Application in aerial image-based system --- p.60Chapter CHAPTER 6 --- conclusion --- p.62bibliography --- p.63appendix a sobel edge detection --- p.66appendix b neural network setup --- p.67appendix c numerical results --- p.6