engleski

Multi-object Tracking via Gaussian Distribution Mixtures

Multi-object tracking is a complex problem where the goal is to estimate the number of objects in the surveillance region together with the kinematic states of each object. In addition to the measurement and process model uncertainty from classical estimation, there are many additional sources of uncertainty in multi-object tracking. For example, the number of objects may change with time and some objects might be undetected in some frames. Usually, there is also no way to determine which detections correspond to which object, hence a data association method is required in such problems. By using random finite sets theory, it is possible to extend the Bayesian recursion to the multi-object case. However, there exists no general closed-form solutions. Nonetheless, there are many approximations to the multi-object Bayesian recursion based on Gaussian mixtures. The goal of this thesis is to implement a Gaussian mixture multi-object tracking algorithm in MATLAB and test it in simulations.

Multi-object tracking ; Bayesian state estimation ; Random Finite Sets ; PHD filter ; Gaussian component reduction ;

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