Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical flow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments.
Abstract. Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. This requirement raises many issues for existing approaches to software verification. An approach based on behavior-based controllers in a process-algebra framework is proposed by Lyons et al [15] to side-step state combinatorics. In this paper we show that this approach can be used to generate a Dynamic Bayesian Network for the problem, and that verification is reduced to a filtering problem for this network. We present validation results for the verification of a multiple waypoint robot mission using this approach.
Abstract-This paper describes the need and methods required to construct an integrated software verification and mission specification system for use in robotic missions intended for counter-weapons of mass destruction (c-WMD) operations, as part of a 3-year effort for the Defense Threat Reduction Agency. The overall system architecture is described. The principal tool for verification is a process algebra, PARS, based on port automata theory. PARS is introduced, emphasizing its ability to represent probabilistic programs and uncertain and dynamic environments, followed by the analysis of mission properties for an example robotic mission.
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