Conventional cameras are usually small in their field of view (FOV) and make the observable region limited. Applications by such a vision system may also limit motion capabilities for robots when it comes to object tracking. Omnidirectional camera has a wide FOV which can obtain environmental data from all directions. In comparison with conventional cameras, the wide FOV of omnidirectional cameras reduces blind regions and improves tracking ability. In this thesis, we assume an omnidirectional camera is mounted on a moving platform, which travels with planar motion. By applying optical flow and CAMShift algorithm to track an object which is non-propelled and only subjected to gravity. Then, by parabolic fitting, least-square method and Levenberg-Marquardt method to predict the 3D coordinate of the object at the current instant and the next instant, we can finally predict the position of the drop point and drive the moving platform to meet the object at the drop point. The tracking operation and drop point prediction can be successfully achieved even if the camera is under planar motion and rotation.

A tracking system that serves as a tool for tracking the movement of surgical instruments has been developed. The system tracks color markers on the surgical instruments. The Continuously Adaptive Mean Shift (CAMSHIFT) methodology was employed for tool tracking and a total distance traversed by the surgical instrument of interest was calculated. Two cameras were used to record the motion of the tool and the software developed was used to track the movement of markers on the tools over subsequent frames. The information thus derived from the two views of cameras was used to calculate the three dimensional coordinates of the location of the marker on the instrument and subsequently the distance traversed. MATLAB, which is a commercial software package, was used to implement the tool tracking algorithm and for developing the GUI (Graphic User Interface). Data was collected using Commercial off the shelf (COTS) camera hardware and processing was done on a 2.2 GHz, 512 MB RAM Intel Pentium 4 computer.

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / Flight tests and flight safety are tightly coupled. For a given experimental test flight, the main objective to be achieved is related to the test bed and crew integrity (i.e. Bring back the test bed in one piece). Furthermore, the aircraft operational safety (i.e. continued airworthiness) relies into the accuracy of gathered information, provided by the airborne Flight Test Instrumentation (FTI) system. Typically, the FTI system includes: a data acquisition system; a transmission system; a data recorder; multiple sensors and auxiliary equipment such as Global Navigation Satellite System (GNSS) Receiver. In a specific Flight Test Campaign (FTC), such as external stores separation FTC, the associated risk is high because the non-linear and unpredicted aerodynamics couplings between the released store and the aircraft structure (e.g. wings) could lead to a catastrophic collision condition. Typically, the setup for such FTC is a big technical challenge for the instrumentation group because the determination of six degrees of freedom (6Dof) trajectory of the release store becomes mandatory. Such information could be by the means of an optical tracking system that requires the integration of two or more hi-speed hi-resolution video cameras into FTI. Unfortunately, the FTI and the Real-Time Telemetry Link (RTL) limited bandwidth imposes several restrictions for the development and implementation of a real-time application to be used at the Ground Telemetry System (GTS) for the verification of the separation safety and the validation of the test point. This paper, presents the implementation of computer vision techniques and Camshift algorithm as an approach to tracking individual track targets on the store surface which can be perform onboard and in real time or near real time. The development of the algorithm is presented as well several experimental test results that use videos frames gathered from previous FTC executed by Instituto de Pesquisas e Ensaios em Voo (Flight Test and Research Institute - IPEV). Preliminary results present satisfactory performance.

The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.

The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.

The use of hand gestures provides an attractive alternative to cumbersome interface devices such as keyboard, mouse, joystick, etc. Hand tracking has a great potential as a tool for better human-computer interaction by means of communication in a more natural and articulate way. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures and hand tracking. In this study, a real-time hand tracking system is developed. Mainly, it is image-based hand tracking and based on 2D image information. For separation and identification of finger parts, coloured markers are used. In order to obtain 3D tracking, a stereo vision approach is used where third dimension is obtained by depth information. In order to see results in 3D, a 3D hand model is developed and Java 3D is used as the 3D environment. Tracking is tested on two different types of camera: a cheap USB web camera and Sony FCB-IX47AP camera, connected to the Matrox Meteor frame grabber with a standard Intel Pentium based personal computer. Coding is done by Borland C++ Builder 6.0 and Intel Image Processing and Open Source Computer Vision (OpenCV) library are used as well. For both camera types, tracking is found to be robust and efficient where hand tracking at ~8 fps could be achieved. Although the current progress is encouraging, further theoretical as well as computational advances are needed for this highly complex task of hand tracking.

This study has put three different object detecting techniques to the test. The goal was to investigate small and fast-moving objects to see which technique’s performance is most suitable within the sports of Padel. The study aims to cover and explain different affecting conditions that could cause better but also worse performance for small and fast object detection. The three techniques use different approaches for detecting one or multiple objects and could be a guideline for future object detection development. The proposed techniques utilize background histogram calculation, HSV masking with edge detection and DNN frameworks together with the COCO dataset. The process is tested through outdoor video footage across all techniques to generate data, which indicates that Canny edge detection is a prominent suggestion for further research given its high detection rate. However, YOLO shows excellent potential for multiple object detection at a very high confidence grade, which provides reliable and accurate detection of a targeted object. This study’s conclusion is that depending on what the end purpose aims to achieve, Canny and YOLO have potential for future small and fast object detection.