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An implicit shape model based approach to identify armed persons

: Becker, S.; Jüngling, K.

Postprint urn:nbn:de:0011-n-1824136 (222 KByte PDF)
MD5 Fingerprint: b2aef32d96f0053e12cd2208dbaeae60
Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 14.10.2011

Sadjadi, F.A. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Automatic Target Recognition XXI : SPIE Defense, Security and Sensing 2011: 26. April 2011 - 28. April 2011, Orlando, Florida, USA
Bellingham, WA: SPIE, 2011 (Proceedings of SPIE 8049)
ISBN: 978-0-8194-8623-3
Paper 80490O, 10 S.
Conference "Automatic Target Recognition" <21, 2011, Orlando/Fla.>
Defense, Security and Sensing Symposium <2011, Orlando/Fla.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
SIFT; ISM; object detection; weapon detection

In addition to detecting and tracking persons via video surveillance in public spaces like airports and train stations, another important aspect of a situation analysis is the appearance of objects in the periphery of a person. Not only from a military perspective, in certain environments, an unidentified armed person can be an indicator for a potential threat. In order to become aware of an unidentified armed person and to initiate counteractive measures, the ability to identify persons carrying weapons is needed. In this paper we present a classification approach, which fits into an Implicit Shape Model (ISM) based person detection and is capable to differentiate between unarmed persons and persons in an aiming body posture. The approach relies on SIFT features and thus is completely independent of sensor-specific features which might only be perceivable in the visible spectrum. For person representation and detection, a generalized appearance codebook is used. Compared to a stand-alone person detection strategy with ISM, an additional training step is introduced that allows interpretation of a person hypothesis delivered by the ISM. During training, the codebook activations and positions of participated features are stored for the desired classes, in this case, persons in an aiming posture and unarmed persons. With the stored information, one is able to calculate weight factors for every feature participating in a person hypothesis in order to derive a specific classification model. The introduced model is validated using an infrared dataset which shows persons in aiming and non-aiming body postures from different angles.