Essays about: "Gaussian Mixture probability Hypothesis Density Filter"
Found 4 essays containing the words Gaussian Mixture probability Hypothesis Density Filter.
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1. Anomaly detection in videosurveillance feeds
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : Traditional passive surveillance is proving ineffective as the number of available cameras for an operator often exceeds the operators ability to monitor them. Furthermore, monitoring surveillance cameras requires a focus that operators can only uphold for a short amount of time. READ MORE
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2. The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System
University essay from Blekinge Tekniska Högskola/Sektionen för ingenjörsvetenskapAbstract : In this thesis, the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter using a pair of stereo vision system to overcome label discontinuity and robust tracking in an Intelligent Vision Agent System (IVAS) is evaluated. This filter is widely used in multiple-target tracking applications such as surveillance, human tracking, radar, and etc. READ MORE
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3. Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach
University essay from Blekinge Tekniska Högskola/Sektionen för ingenjörsvetenskapAbstract : Motion tracking is an important part of the Intelligent Vision Agent System, IVAS. In this thesis, the Gaussian mixture approximation of the Probability Hypothesis Density filter (GM-PHD) was implemented to provide a reliable and computationally efficient multiple human tracker in the activity space of the IVAS. READ MORE
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4. Birth Density Modeling in Multi-target Tracking Using the Gaussian Mixture PHD Filter
University essay from Blekinge Tekniska Högskola/Avdelningen för signalbehandlingAbstract : A recently established method for multi-target tracking which both estimates the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms is the probability hypothesis density (PHD) recursion. The approach involves modeling the respective collections of targets and measurements as random finite sets and to propagate the posterior intensity, which is a first order statistic of the random finite set of targets, in time. READ MORE
