Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging. In this talk, we discuss the key challenges and current approaches for person re-identification and tracking, as well as our initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. We also briefly discuss privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.