@inbook{2e94647911fc44e4ae943c4e6b26016e,
title = "Contextual Tracking in Surface Applications: Algorithms and Design Examples",
abstract = "In this chapter, contextual information is discussed for improving tracking of surface vehicles. Contextual information generally involves any kind of information that is not related directly to kinematic sensor measurements. This information, termed trafficability, is used to incorporate constraints on the vehicle that ultimately deflect the tracks to areas that provide the highest trafficable regions. For example, local terrain slope, ground vegetation and other factors that put constraints on the vehicles can be considered as contextual information. Both kinematic sensor data and contextual information are tied into the overall tracker design through the use of trafficability maps. Two specific design examples are summarized in this chapter. The first example involves ground tracking of vehicles where the contextual information exploits terrain information to aid in the tracking. The second example involves a sea-based maritime application where the contextual information exploits depth, marked shipping channel locations, and high-value unit information as contextual information. Both examples show that the use contextual information can significantly improve tracking performance.",
keywords = "Contextual tracking, Contextual-based tracker (ConTracker), Kalman filtering, Trafficable",
author = "Fosbury, \{Adam M.\} and Crassidis, \{John L.\} and Jemin George",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland (outside the USA) 2016.",
year = "2016",
doi = "10.1007/978-3-319-28971-7\_13",
language = "English",
series = "Advances in Computer Vision and Pattern Recognition",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "339--379",
booktitle = "Advances in Computer Vision and Pattern Recognition",
address = "Germany",
}