We present an algorithm which tracks multiple objects for video surveillance applications. This algorithm is based on a Bayesian framework and a Particle filter. In order to use this method in practical applications we define a statistical model of the object appearance to build a robust likelihood function. The tracking process is only based on image data, therefore, a previous step to learn the object shape and their motion parameters is not necessary. Using the localization results, we can define a prior density which is used to initialize the algorithm. Finally, our method has been proved successfully in several sequences and its performance is more accurate than classical filters. © Springer-Verlag Berlin Heidelberg 2003.
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 1 Dec 2003|