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Tracking across cameras with non-overlapping views is a challenging problem. Firstly, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in(More)
Conventional tracking approaches assume proximity in space, time and appearance of objects in successive observations. However, observations of objects are often widely separated in time and space when viewed from multiple non-overlapping cameras. To address this problem, we present a novel approach for establishing object correspondence across(More)
—This paper presents a method to perform a high-level segmentation of videos into scenes. A scene can be defined as a subdivision of a play in which either the setting is fixed, or when it presents continuous action in one place. We exploit this fact and propose a novel approach for clustering shots into scenes by transforming this task into a graph(More)
— This paper presents a framework for the classification of feature films into genres, based only on computable visual cues. We view the work as a step towards high-level semantic film interpretation, currently using low-level video features and knowledge of ubiquitous cinematic practices. Our current domain of study is the movie preview, commercial(More)
In this paper, we present a wide area surveillance system that detects, tracks and classifies moving objects across multiple cameras. At the single camera level, tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence. The system uses the single camera tracking results along with the relationship(More)
This paper describes how model-specific constraints and domain specific knowledge can be utilized to develop efficient sampling based algorithms for robust model estimation in the presence of out-liers. As a special case, a robust algorithm for homography estimation is proposed that exploits the invariance of collinearity under homogra-phy to improve(More)
Multiple cameras are needed to completely cover an environment for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial(More)