Abstract. Situation awareness (SAW) systems aim at supporting assessment of critical situations as, e.g., needed in traffic control centers, in order to reduce the massive information overload. When assessing situations in such control centers, SAW systems have to cope with a large number of heterogeneous but interrelated real-world objects stemming from various sources, which evolve over time and space. These specific requirements harden the selection of adequate data mining techniques, such as clustering, complementing situation assessment through a datadriven approach by facilitating configuration of the critical situations to be monitored. Thus, this paper aims at presenting a survey on clustering approaches suitable for SAW systems. As a prerequisite for a systematic comparison, criteria are derived reflecting the specific requirements of SAW systems and clustering techniques. These criteria are employed in order to evaluate a carefully selected set of clustering approaches, summarizing the approaches' strengths and shortcomings.

A GM-PHD filter is used for pedestrian tracking in a crowd surveillance application. The purpose is to keep track of the different groups over time as well as to represent the shape of the groups and the number of people within the groups. In-put data to the GM-PHD filter are detections using a state of the art algorithm applied to video frames from the PETS 2012 benchmark data. In a first step, the detections in the frames are converted from image coordinates to world coordinates. This implies that groups can be defined in physical units in terms of distance in meters and speed differences in meters per second. The GM-PHD filter is a Bayesian framework that does not form tracks of individuals. Its output is well suited for clustering of individuals into groups. The results demon-strate that the GM-PHD filter has the capability of estimating the correct number of groups with an accurate representation of their sizes and shapes. Index Terms — Multi target tracking, group target track-ing, GM-PHD, groups. 1.