Wide Area Tracking in Single and Multiple Views

Abstract

Maintaining the stability of tracks on multiple targets in video over extended time periods and wide areas remains a challenging problem. Basic trackers like the Kalman filter or particle filter deteriorate in performance as the complexity of the scene increases. A few methods have recently shown encouraging results in these application domains. They rely on learning context models, the availability of training data, or modeling the inter-relationships between the tracks. In this chapter, we provide an overview of research in the area of long-term tracking in video. We review some of the methods in the literature and analyze the common sources of errors which cause trackers to fail.We also discuss the limits of performance of the trackers as multiple objects come together to form groups and crowds. On multiple real-life video sequences obtained for a single camera as well as a camera network, we compare the performance of some of the methods.