Importance of detection for video surveillance applications

Though it is the first step of a real video surveillance applica- tion, 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 sta- tistical model of the object appearance to build a robust likelihood func- tion. Likewise, we present an extension of a background subtraction al- gorithm 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 evalu- ated to elucidate the importance of detection for video surveillance applications. © 2008 Society of Photo-Optical Instrumentation Engineers.