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Object detection in MOUT: Evaluation of a hybrid approach for confirmation and rejection of object detection hypotheses

: Manger, Daniel; Metzler, Jürgen

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Erstellt am: 31.3.2015

Niel, K.S. ; Society for Imaging Science and Technology -IS&T-; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image processing: machine vision applications VII : 3 - 4 February 2014, San Francisco, California, United States
Bellingham, WA: SPIE, 2014 (Proceedings of SPIE 9024)
ISBN: 978-0-8194-9941-7
Paper 90240P, 5 S.
Conference "Image Processing - Machine Vision Applications" <7, 2014, San Francisco/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
MOUT; person and object detection; discriminative and generative models; hybrid detection

Military Operations in Urban Terrain (MOUT) require the capability to perceive and to analyze the situation around a patrol in order to recognize potential threats. A permanent monitoring of the surrounding area is essential in order to appropriately react to the given situation, where one relevant task is the detection of objects that can pose a threat. Especially the robust detection of persons is important, as in MOUT scenarios threats usually arise from persons. This task can be supported by image processing systems. However, depending on the scenario, person detection in MOUT can be challenging, e.g. persons are often occluded in complex outdoor scenes and the person detection also suffers from low image resolution. Furthermore, there are several requirements on person detection systems for MOUT such as the detection of non-moving persons, as they can be a part of an ambush. Existing detectors therefore have to operate on single images with low thresholds for detection in order to not miss any person. This, in turn, leads to a comparatively high number of false positive detections which renders an automatic vision-based threat detection system ineffective. In this paper, a hybrid detection approach is presented. A combination of a discriminative and a generative model is examined. The objective is to increase the accuracy of existing detectors by integrating a separate hypotheses confirmation and rejection step which is built by a discriminative and generative model. This enables the overall detection system to make use of both the discriminative power and the capability to detect partly hidden objects with the models. The approach is evaluated on benchmark data sets generated from real-world image sequences captured during MOUT exercises. The extension shows a significant improvement of the false positive detection rate.