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A study of individual dynamics for Multi-Target tracking using random finite sets

Grant number: 10/00055-1
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): May 01, 2010
Effective date (End): March 31, 2014
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal researcher:João Bosco Ribeiro do Val
Grantee:Victor Baptista Frencl
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The problem of target tracking is handled in different ways in the literature, either developing more efficient mathematical models to reproduce the dynamics of movements, or building stochastic filters that perform state estimation, such as position and velocity. When it comes to target tracking where the targets of interest are many individuals in motion, the literature lacks on specific studies. Thus, the main objective of the project is to deepen the knowledge of individuals tracking. In this scenario, there is a large and variable number of targets, which may arise spontaneously, group together or separate, in addition to measures immersed in false alarms. A study of the Random Finite Sets theory is made, whose mathematical treatment is based on the so-called Multi-Target Calculus. Stochastic filters is also studied from this point of view, where the PHD and the GM-PHD filters are the main ones. After created the theoretical basis, three proposals based on this problem will be presented: Motion Models for Individuals, a Simulator for Individuals Trajectories and Dynamic Models for Stochastic Filtering. The first proposal is based on building a probabilistic motion shape for each individual. The second proposal involves the creation of a trajectory simulator for individuals to be as plausible as possible to the real movements of a person, in scenarios with variations of terrain, ranked by locomotion difficulty. Finally, the last proposal aims to create a combined and modified model from some models found in the literature, to be the dynamic base of the stochastic filters. Finally, several tests and simulations will be made in such a way to test filter performances and to analyze the behavior of the proposed mathematical models and the probabilistic motion shapes. (AU)

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