With the increase in the availability and computational power of digital imaging devices, it is natural to think about integrating artificial intelligence models with processing of digital images and videos used for visual surveillance applications to improve their performance. However, for such applications to be efficient, there is a need for addressing a number of significant challenges. Accounting for the presence of regions that do not belong to objects of interest, global ambient illumination variations in the environment over long periods of time, and the real-time constraints inherent to visual surveillance applications are among such obstacles. This chapter demonstrates two main categories of mathematical and computational techniques developed to help improve the accuracy and efficiency of automated visual surveillance systems.