The aim of this paper is to propose an artificial intelligence based approach to moving object detection and tracking. Specifically, we adopt an approach to moving object detection based on self organization through artificial neural networks. Such approach allows to handle scenes containing moving backgrounds and gradual illumination variations, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, for object tracking we propose a suitable conjunction between Kalman filtering, properly instanced for the problem at hand, and a matching model belonging to the class of Multiple Hypothesis Testing. To assess the validity of our approach, we experimented both proposed moving object detection and object tracking over different color video sequences that represent typical situations critical for video surveillance systems.
|Titolo:||Object Motion Detection And Tracking by An Articial Intelligence Approach|
FERONE, ALESSIO (Ultimo)
|Parole Chiave:||Motion detection; Multiple hypothesis testing; Object tracking; Self organization; Visual surveillance|
|Data di pubblicazione:||ago-2008|
|Digital Object Identifier (DOI):||10.1142/S0218001408006612|
|Appare nelle tipologie:||01 - Articolo su periodico|