A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

• We support theoretically the Monte Carlo approach to 3D visual tracking.
• We compare two Monte Carlo algorithms: a Viterbi-based and a Particle Smoother.
• The Particle Smoother performs better thanks to its continuous state representation.

Visual vehicular trajectory analysis and reconstruction represent two relevant tasks both for safety and capacity concerns in road transportation. Especially in the presence of roundabouts, the perspective effects on vehicles projection on the image plane can be overcome by reconstructing their 3D positions with a 3D tracking algorithm. In this paper we compare two different Monte Carlo approaches to 3D model-based tracking: the Viterbi algorithm and the Particle Smoother. We tested the algorithms on a simulated dataset and on real data collected in one working roundabout with two different setups (single and multiple cameras). The Viterbi algorithm estimates the Maximum A-Posteriori solution from a sample-based state discretization, but, thanks to its continuous state representation, the Particle Smoother overcomes the Viterbi algorithm showing better performance and accuracy.