Bayesian Tracking for Video Analytics

Visual tracking represents the basic processing step for most video analytics applications where the aim is to automatically understand the actions occurring in a monitored scene. Consequently, the performances of these applications are significantly dependent on the accuracy and robustness of the tracking algorithm. Bayesian state estimation and probabilistic graphical models (PGMs) have proved to be very powerful 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 proposed. This categorization intends to logically organize different concepts related to Bayesian visual tracking to give a global overview 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.