Jaime Ortegon-Aguilar, Eduardo Bayro-Corrochano, "Particle Filter Tracking without Dynamics", Applied Bionics and Biomechanics, vol. 4, Article ID 417219, 9 pages, 2007. https://doi.org/10.1080/11762320701843499
Particle Filter Tracking without Dynamics
People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate (one or two frames per second). Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.
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