Search for dissertations about: "multi-target"
Showing result 1 - 5 of 12 swedish dissertations containing the word multi-target.
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1. Multi-target Tracking Using on-line Viterbi Optimisation and Stochastic Modelling
Abstract : To study and compare the safety of intersection, traffic scientists today typically manually monitor the intersection during several days and count how often certain events such as evasive manoeuvres occur. This is a laboursome and costly procedure. READ MORE
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2. Multiple Model Filtering and Data Association with Application to Ground Target Tracking
Abstract : This thesis is concerned with two central parts of a tracking system, namely multiple-model filtering and data association. Multiple models are introduced to provide accurate filtering, whereas data association deals with the unknown origin of the received measurements. READ MORE
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3. Computer Vision for Automated Traffic Safety Assessment : A Machine Learning Approach
Abstract : Traffic safety is a complex and important research area with the potential to save many lives in the future. Two key problems are considered, namely the gathering of reliable and detailed road user statistics which can be used to estimate the safety of a traffic environment and taking advantage of surveillance infrastructure to guide and assist vehicles in real time, primarily autonomous ones. READ MORE
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4. Silicon nanowire based devices for More than Moore Applications
Abstract : Silicon nanowires (SiNW) are in the spotlight for a few years in the research community as a good candidate for biosensing applications. This is attributed to their small dimensions in nanometer scale that offers high sensitivity, label-free detection and at the same time utilizing small amount of sample. READ MORE
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5. Simulation Based Methods for Target Tracking
Abstract : In this thesis we study a Bayesian estimation formulation of the target tracking problem. Traditionally, linear or linearized models are used, where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. Hence, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. READ MORE