Importance of detection for video surveillance applications

Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi object tracking algorithm based on a Bayesian framework and a particle filter. This algorithm, which we have named iTrack, is specifically designed to work in practical applications by defining a statistical model of the object appearance to build a robust likelihood function. Likewise, we present an extension of a background subtraction algorithm to deal with active cameras. This algorithm is used in the detection task to initialize the tracker by means of a prior density. By defining appropriate performance metrics, the overall system is evaluated to elucidate the importance of detection for video surveillance applications.This work was supported by the project 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069). This work is supported by EC grants IST-027110 for the HERMES project and IST-045547 for the VIDI video project, and by the Spanish MEC under projects TIN2006-14606, TIN2007-67896, and CONSOLIDER-INGENIO 2010 CSD2007-00018 . Jordi Gonzàlez and Javier Varona also acknowledge the support of a Juan de la Cierva and a Ramon y Cajal cofunded by the European Social Fund Postdoctoral Fellowship from the Spanish MEC, respectively.Peer Reviewe