@TechReport{TR071,
  author =	 "Roman Pflugfelder",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-071",
  title =	 "Visual {T}raffic {S}urveillance {U}sing {R}eal-time
                  {T}racking",
  year =	 "2002",
  url =		 "https://www.prip.tuwien.ac.at/pripfiles/trs/tr71.pdf",
  abstract =	 "This report presents an unusual event recognition
                  approach in the field of traffic surveillance. Such
                  events are unusual traffic behaviour like traffic
                  jams, accidents or ghost drivers. An interest-point
                  based tracking algorithm (KLT-tracker) is discussed
                  which pursues features on vehicles through a static
                  camera scene. Tracking data can be collected by
                  observing normal traffic. Then, this data is used to
                  learn a spatio-temporal model of normal traffic
                  behaviour. Thereby, training samples are generated
                  in a learning space by the tracking data. Thus, the
                  spherical probability density function (p.d.f.) of
                  the space can be estimated. We use a Growing Neural
                  Gas in combination with a MDL-based pruning
                  algorithm for unsupervised learning. The former
                  method belongs to the class of soft-competitive
                  algorithms which overcome the problems of stranded
                  reference vectors. In contrast to other works, the
                  number of reference vectors has not to be
                  constant. The algorithm finds an optimal codebook
                  according to the MDL-principle. As the p.d.f. only
                  describes points and not trajectories of normal
                  traffic behaviour, behaviour classes of normal
                  traffic have to be learnt additionally. This work
                  presents a novel approach by using the topology of
                  the learning space which is created by Competitive
                  Hebbian Learning. Beside the necessity of
                  recognizing unusual events, it can also be used to
                  analyze the behaviour of drivers at traffic sites
                  like intersections or road works. ",
}