The tracking of individuals in cluttered scenes has been of much interest in Machine Vision research for more than a decade. A number of algorithms have been devised to track individuals and some of the algorithms have attempted the tracking of large groups of people. In this paper we discuss how to generate automatic maps of trends of movement in complex scenes, without the use of tracking. In...

This paper presents a new approach to optimising the rendering of large crowded scenes for use in virtual environment walk-through applications. We combine an existing impostor rendering technique with new approximated visibility techniques to display scenes populated with large numbers of virtual humans.

A Scale Invariant Feature Transform (SIFT) based on particle filter algorithm is presented for object tracking. We propose a new algorithm for object tracking in crowded video scenes by exploiting the properties of Undecimated Wavelet Packet Transform (UWPT) and particle filter. SIFT features are used to correspond the region of interests across frames. Meanwhile, feature vectors generated via ...

In this paper we address the problem of semantic analysis of structured/unstructured crowded video scenes. Our proposed approach relies on tracklets for motion representation. Each extracted tracklet is abstracted as a directed line segment, and a novel tracklet similarity measure is formulated based on line geometry. For analysis, we apply non-parametric clustering on the extracted tracklets. ...