Content Provider	IEEE Xplore Digital Library
Author	Emonet, Remi Oberzaucher, E. Odobez, J.-M.
Copyright Year	2014
Description	Author affiliation: Dpt of Anthropology, University of Vienna, Austria (Oberzaucher, E.) \|\| Laboratoire Hubert Curien UMR CNRS 5516, Université Jean Monnet, St-Etienne, France (Emonet, Remi) \|\| Idiap Research Institute, 1920 Martigny, Switzerland (Odobez, J.-M.)
Abstract	The installation of surveillance networks has been growing exponentially in the last decade. In practice, videos from large surveillance networks are almost never watched, and it is frequent to see surveillance video wall monitors showing empty scenes. There is thus a need to design methods to continuously select streams to be shown to human operators. This paper addresses this issue and make three main contributions: it introduces and investigates, for the first time in the literature, the live stream selection task; based on the theory of social attention, it formalizes a way of obtaining some ground truth for the task and hence a way of evaluating stream selection algorithms; and finally, it proposes a two-step approach to solve this task and compares different approaches for interestingness rating using our framework. Experiments conducted on 9 cameras from a metro station and 5 hours of data randomly selected over one week show that, while complex unsupervised activity modeling algorithms achieve good performance, simpler approaches based on amount of motion perform almost as well for this type of indoor setting.
Starting Page	433
Ending Page	440
File Size	2435230
Page Count	8
File Format	PDF
ISBN	9789897581335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-05
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	SCITEPRESS
Subject Keyword	Presses Camera Network Surveillance Temporal Topic Models Probabilistic Models Stream Selection Cameras Motion pictures Robustness Videos
Content Type	Text
Resource Type	Article

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