A Multi-Target Tracking and GMM-Classifier for Intelligent Vehicles

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Abstract
Intelligent vehicles need reliable information about the environment in order to operate with total safety. In this paper we propose a flexible multi-module architecture for a multi-target detection and tracking system (MTDTS) complemented with a Bayesian object classification layer based on finite Gaussian mixture models (GMM). The GMM parameters are estimated by an expectation maximization (EM) algorithm, hence finite-component models were generated based on feature-vectors extracted from object's classes during the training stage. Using the joint mixture Gaussian pdf modelled for each class, a Bayesian approach is used to distinct the object's categories (persons, tree-trunks/posts, and cars) in a semi-structured outdoor environment based on data from a laser range finder (LRF). Experiments using real data scan confirm the robustness of the proposed architecture. This paper investigates a particular problem: detection, tracking and classification of objects in cybercars-like outdoor environments

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