IEEE SIGNAL PROCESSING MAGAZINE [46] SEPTEMBER 2010 Digital Object Identifier 10.1109/MSP.2010.937395 1053-5888/10/$26.00©2010IEEE Bayesian Tracking for Video Analytics [ An overview ] [ Alessio Dore, Mauricio Soto, and Carlo S. Regazzoni ] isual tracking represents the basic process- ing step for most video analytics applica- tions where the aim is to automatically understand the actions occurring in a monitored scene. Consequently, the perfor- mances of these applications are significantly depen- dent on the accuracy and robustness of the tracking algorithm. Bayesian state estimation and probabilistic graphical models (PGMs) have proved to be very pow- erful and appropriate mathematical tools to efficiently solve the inference problem of motion estimation by combining object dynamics and observations. In this article, the impact of these signal processing techniques on the development of recent tracking algorithms is shown and a categorization of the most common approaches is pro- posed. This categorization intends to logically organize different concepts related to Bayesian visual tracking to give a global over- view to the reader. Finally, general considerations on the design of visual trackers for video analytics systems are discussed, focusing on the tradeoff that is usually performed between the accuracy of the target motion assumptions and the robustness of the object appearance representation. INTRODUCTION The basic tracking task consists in estimating the trajectory of a moving object by consistently assigning a label over frames con- sidering noisy measurements. Therefore, tracking can be consid- ered as a filtering process that eliminates the noise from measurements to derive its motion. In this context, enhanced tracking algorithms have been investigated for many applications such as human computer interaction, automated surveillance, traffic monitoring, and automotive preventive safety. For example in [1]–[3], complex industrial surveillance systems are presented pointing out the paramount importance of reliable tracking accu- racy to ensure acceptable performances in the monitoring task. Recently, researchers focused on developing accurate trackers able to obtain a rich object description with the aim of accomplish- ing advanced scene interpretation tasks. This trend is driven by the growing interest in developing video analysis systems for helping and supporting humans in many assignments. To achieve this, a versatile, flexible, and expressive framework is required where the differences between algorithms can be easily understood. Additionally, coherent and consistent paradigms should be employed to obtain robust, reliable, and efficient algorithms. In this regard, the application of Bayesian filtering algorithms to PGMs can provide useful tools for visual tracking. PGMs are able to provide an appropriate theoretical framework where object dynamics and appearance can be combined and the motion estimation problem can be efficiently solved. As a matter of fact, PGMs can represent, learn, and compute complex probabil- ity distributions by explicitly defining statistical dependencies between the elements of the probability model. Then by combin- ing graph theory and probability theory, PGMs aim to deal with © BRAND X PICTURES V