Fusing appearance and spatio-temporal features for multiple camera tracking

Abstract

Multiple camera tracking is a challenging task for many surveillance systems. The objective of multiple camera tracking is to maintain trajectories of objects in the camera network. Due to ambiguities in appearance of objects, it is challenging to re-identify objects when they re-appear in other cameras. Most research works associate objects by using appearance features. In this work, we fuse appearance and spatio-temporal features for person re-identification. Our framework consists of two steps: preprocessing to reduce the number of association candidates and associating objects by using the probabilistic relative distance. We set up an experimental environment including 10 cameras and achieve a better performance than using appearance features only. © 2014 Springer International Publishing.