A new method for 3D vehicle modeling in low-cost monocamera surveillance systems is introduced in this paper. The proposed algorithm aims to resolve the projective ambiguity of 2D image observations by means of the integration of temporal information and model priors within a Markov Chain Monte Carlo (MCMC) method. The method is specially designed to work in challenging scenarios, with noisy and blurred 2D observations, where traditional edge-fitting or feature-based methods fail. Tests have shown excellent estimation results for traffic-flow video surveillance applications, that can be used to classify vehicles according to their length, width and height.
In REF , a 3D vehicle modeling has been proposed for detection and classification by means of the integration of temporal information and model priors within Figure 1 : Proposed CRN model.
REAL-TIME 3D MODELING OF VEHICLES IN LOW-COST MONOCAMERA SYSTEMS
VISAPP
Computer Science