To overcome the hurdles associated with space management and security controls in a housing system, research was projected to study and analyze the necessary factors of accomplishment. Over time, different processes were observed and reviewed to make this a possible deal. Various residents were interviewed on the daily constraints in parking and managing their vehicles within their housing premises. The reported daunting concern was majorly the gate access and personal hunts for the space to keep the individual resident’s cars. Every resident would always have to stop and hoot at the housing gate for the assigned personnel to check and open the gate. While this would waste every resident’s time, the visitors even face more delay often time. Hitherto, car access and parking constraint become a thing of worry that no one would want to engage the housing service anymore. The interest has got dwindled. And to re-awaken the high patronage of the housing system, a gap must be bridged with an immediate solution to space management with a gating system. These were subsequently given a classical thought, while a prototype solution was demonstrated and reviewed with the various residents of some selected housing. This received a high welcoming embracement and was beckoned to be made real by the logical heuristic. At this point, nothing was further considered than using the Internet of things (IoT) technology to implement Vehicular Access Management for the control and integration of intended space provisioning in any housings. Consequently, the number plate of every vehicle becomes the automatic access tag and would be used for security control within the housing location. Vehicles’ numbers would be captured and used to manage the residents passing through the automated gating system. With it, records would be made for all permitted residents and the visitors that own a car. Thus, a proper arrangement would be allotted accordingly, as provisioned by the gating system administrator. However, to allegories the above-proffered solution, this project work is divided into six sections. The introductory section introduces the project rationale, lists the objectives, explores related works, and introduces how IoT and vehicular systems can be merged. The second section delves into these vehicular systems. It introduces the Automatic License Plate Recognition System (ALRP) and the Raspberry Pi and highlights the merits of the Integrated Vehicular Access Security System. Open-CV and machine learning are also introduced. Section three covers the solution design, while section four is the implementation phase. Section five covers the testing and implementation of the solution. The final section summarizes the project. The project successfully models an automated solution for the security of tenants and vehicle users against unauthorized access to residential estates and buildings.

This master thesis is engaged in development of software tool intended for measuring of status of lower limb bones. It analyzes status of the feet and lower limb and defines significantpointsonthem.Alsoanalyzesdesignoptionsofmeasuringsceneandmarkers and describe the one selected. In another part is described automated point recognition algorithm. The last part describes graphical user interface, where are these algorithms implemented.

This thesis describes the design of the image processing for Android system, consisting of the choice of the development environment and its implementation. Workflow soluti-on to the problem involves development of the Androidapplication and it’s graphical user interface. The text includes description of the application functionality, communica-tionwith a camera, storing and retrieving data. It also describes used algo-rithms and image processing methods used for detecting values from the counter of the gas meter.

Context : Air being the free source is used in different ways commercially. In earlier days windmills generate power, water, and electricity. The excessive establishment of windmills for commercial purposes affected avifauna. Most of the birds lost their lives due to collisions with windmills. Turbines used to generate power near airports are also one of the causes for the extinction of birdlife. According to a survey in 2011 in Canada a total of 23,300 bird deaths were caused by wind turbines and also it is estimated that the number of deaths would increase to 2,33,000 in the following 10-15 years. Objectives : The main objective of this thesis is to find a suitable software solution to detect the birds on a series of grayscale images in real-time and in minimum full HD resolution with at least a 15 FPS rate. User-Driven Design Methodology is used for developing, tools are Python and Open-CV. Methods : In this research, a system is designed to detect the bird in an HD Video. Possible methods that can be considered are convolutional neural networks (CNN), vision based detection with background subtraction, contour detection and confusion matrix classification. These methods detect birds in raw images and with help of a classifier make it possible to see the bird in desired pixels with full resolution. We will investigate a bird classification method consisting of two steps, background subtraction, and then object classification. Background subtraction is a fundamental method to extract moving objects from a fixed background. For classification, we will use the YOLO v3 model version for object classification. Results : The project is expected to result in a system design and prototype for the bird identification on a grayscale video stream in at least full HD resolution in a minimum of 15 FPS. The bird should be distinguished from other moving objects like wind turbine blades, trees, or clouds. The proposed solution should identify up to 5 birds simultaneously. Conclusion : After completing each step and arriving at the classification, the methods we have tried, such as Haar Cascades and mobile-net SSD, were not providing us with the desired results. So we opted to use YOLO v3, which had the best accuracy in classifying different objects. By using the YOLO v3 classifier, we have detected the bird with 95% accuracy, blades with 90% accuracy, clouds with 80% accuracy, trees with 70% accuracy. Moreover, we conclude that there is a need for further empirical validation of the models in full-scale industry trials.

This thesis describe about determination of camera motion parameters in plane. At first there are introduce the basics of motion tracking, is focused to find out displacement between two input images. Below is describe the algorithm GoodFeatruresToTrack, which find out the most significant point in a first image. The point is search out the good point, which will be easy to track in next image, reduce the data volume and prepare the input information (array of significant point) for the algorithm Lucas-Kanade optical flow. In second part is deal with processing and utilization estimations optical flow. There is median filtration, below is describe computation of homogenous transformation, which describe all affine transformation in affine space. As the result are coordinates, which describe the shift between the two input images as X-axis and Y-axis value. The project used the library Open Computer Vision.