The purpose of the ODS (Occupant Detection System) project is to develop a car safety camera system that is able to discriminate human occupants in order to activate safety features depending on the human size. In order to discriminate the size of an occupant anthropometric measurements need to be performed. The aim of this study was to investigate the possibility of anthropometric measurements of human height and weight with a Kinect for Windows v2 sensor for discrimination of different occupants inside a vehicle. The goal was to find valid anthropometric methods for determination of human height and weight from landmarks on the upper body, then test if it is possible to perform these methods with the Kinect for Windows v2 sensor. The execution of this study was performed as a literature study with anthropometric tests on ATD-dummies (anthropomorphic test device) and on human test subjects. Measurements were performed first physically and then with the Kinect v2 sensor to obtain data in form of distance of 8 regions of the body. Three tests were performed, first on dummies, second was a pilot study and last the measurement study. The result revealed that it is hard to estimate human weight from body landmarks due to lack of information, therefor no tests were performed in this area. For height the result showed that the most valid methods were the measurements on arm span and ulna, both on physical measurements and with the camera. The conclusion is that it is possible estimating height from body landmarks but the positioning of the camera needs to be change in order for the measurements to be more accurate. This study has contributed to a greater understanding of measurement technology, automotive safety and anthropometric measurements.

<div>Numerous organizations around the world deploy sensor networks, especially visual sensor networks for various applications like monitoring traffic, security, and emergencies. With advances in computer vision technology, the potential application of these sensor networks has expanded. This has led to an increase in demand for deployment of large scale sensor networks.</div><div>Sensors in a large network have differences in location, position, hardware, etc. These differences lead to varying usefulness as they provide different quality of information. As an example, consider the cameras deployed by the Department of Transportation (DOT). We want to know whether the same traffic cameras could be used for monitoring the damage by a hurricane.</div><div>Presently, significant manual effort is required to identify useful sensors for different applications. There does not exist an automated system which determines the usefulness of the sensors based on the application. Previous methods on visual sensor networks focus on finding the dependability of sensors based on only the infrastructural and system issues like network congestion, battery failures, hardware failures, etc. These methods do not consider the quality of information from the sensor network. In this paper, we present an automated system which identifies the most useful sensors in a network for a given application. We evaluate our system on 2,500 real-time live sensors from four cities for traffic monitoring and people counting applications. We compare the result of our automated system with the manual score for each camera.</div><div>The results suggest that the proposed system reliably finds useful sensors and it output matches the manual scoring system. It also shows that a camera network deployed for a certain application can also be useful for another application.</div>

The aim of the diploma thesis is to present the main theory concerning high-speed cameras, their usefullness in industry and to present the cameras available at the market. A few experiments have been done focused on the right choice of an objective, lighting of the scene or setting of the cameras. A special equipment has been invented for a better process of measuring followed by experiment aimed at boring a cutter in materials chosen beforehand. The speed and the acceleration of the cutter was ana-lyzed by means of MotionMeasure software in another experiment.