7,841 research outputs found
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
Automatic Color Inspection for Colored Wires in Electric Cables
In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions
Joint Probabilistic People Detection in Overlapping Depth Images
Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations
Multi-Focal Visual Servoing Strategies
Multi-focal vision provides two or more vision devices with different fields of view and measurement accuracies. A main advantage of this concept is a flexible allocation of these sensor resources accounting for the current situational and task performance requirements. Particularly, vision devices with large fields of view and low accuracies can be use
Sensor fusion in smart camera networks for ambient intelligence
This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence
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