Joint target tracking and identification. Part II. Shape video computing
Pierre Minvielle, Alan D. Marrs, Simon R. Maskell, Arnaud Doucet · 2005
For Pt. I see ibid., vol.1 p.256-299, (2005). This paper describes an application of sequential Monte Carlo model-based approaches to perform joint target tracking and identification. While a geometric shape is moving inside the field of view of a CCD camera, alternatively getting closer and moving away while rotating, the data processing system is confronted to challenging tasks: track the moving shape in real 3D space, i.e. estimate its position and orientation, and at the same time dynamically estimate its dimensions and, if required, identify it. The system is based on class-specific Bayesian filters. More originally, the issue of the fixed hyper-parameter estimation, here the geometric shape dimensions, is solved by combining two different techniques. The first one consists of Markov chain Monte Carlo moves that rescale both the trajectory and the shape; it benefits from an efficient statistic which summaries the trajectory with regard to moves. The second one is an artificial deformation diffusion of the shape.