Online Searching with an Autonomous Robot

We discuss online strategies for visibility-based searching for an object hidden behind a corner, using Kurt3D, a real autonomous mobile robot. This task is closely related to a number of well-studied problems. Our robot uses a three-dimensional laser scanner in a stop, scan, plan, go fashion for building a virtual three-dimensional environment. Besides planning trajectories and avoiding obstacles, Kurt3D is capable of identifying objects like a chair. We derive a practically useful and asymptotically optimal strategy that guarantees a competitive ratio of 2, which differs remarkably from the well-studied scenario without the need of stopping for surveying the environment. Our strategy is used by Kurt3D, documented in a separate video.Comment: 16 pages, 8 figures, 12 photographs, 1 table, Latex, submitted for publicatio

Spatio-temporal Video Parsing for Abnormality Detection

Abnormality detection in video poses particular challenges due to the infinite size of the class of all irregular objects and behaviors. Thus no (or by far not enough) abnormal training samples are available and we need to find abnormalities in test data without actually knowing what they are. Nevertheless, the prevailing concept of the field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem, we propose a method for joint detection of abnormalities in videos by spatio-temporal video parsing. The goal of video parsing is to find a set of indispensable normal spatio-temporal object hypotheses that jointly explain all the foreground of a video, while, at the same time, being supported by normal training samples. Consequently, we avoid a direct detection of abnormalities and discover them indirectly as those hypotheses which are needed for covering the foreground without finding an explanation for themselves by normal samples. Abnormalities are localized by MAP inference in a graphical model and we solve it efficiently by formulating it as a convex optimization problem. We experimentally evaluate our approach on several challenging benchmark sets, improving over the state-of-the-art on all standard benchmarks both in terms of abnormality classification and localization.Comment: 15 pages, 12 figures, 3 table

Trumping communitarianism: crime control and forensic DNA typing and databasing in Singapore

Liberalism and communitarianism have figured prominently in discussions of how to govern forensic DNA practices (forensic DNA typing and databasing). Despite the prominence of these two political philosophies and their underlying values, no studies have looked at the governance of forensic DNA practices in a nondemocratic country governed by a communitarian logic. To fill this lacuna in the literature, this article considers Singapore as an authoritarian state governed by a communitarian philosophy. The article highlights basic innovations and technologies of forensic DNA practices and articulates a liberal democratic version of “biolegality” as described by Michael Lynch and Ruth McNally. It goes on to consider briefly various (political) philosophies (liberalism and communitarianism) and law enforcement models (due process and crime control models). The main part of the article records the trajectory, and hence biolegal progress, of forensic DNA practices in Singapore and compares it with trajectories in England and the United States. The article concludes that Singapore's forensic DNA practices are organized according to the crime control model and therefore safety and the war against crime and terrorism trump individual rights and legal principles such as privacy, bodily integrity, proportionality, presumption of innocence. and onus of proof

Finding a door along a wall with an error afflicted robot

We consider the problem of finding a door in a wall with a blind robot, that does not know the distance to the door or whether the door is located left hand or right hand to its start point. This problem can be solved with the well-known doubling strategy yielding an optimal competitive factor of 9 with the assumption, that the robot does not make any errors during its movements. We study the case, that the robots movement is errorneous. We give upper bounds for the movement error, such that reaching the door is guaranteed. More precisely the error range δ has to be smaller than 1/3 . Additionally, the corresponding competitive factor is given by 1 + 8 1+δ / 1−3δ