Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Low resolution person detection with a moving thermal infrared camera by hot spot classification

 
: Teutsch, Michael; Müller, Thomas; Huber, M.; Beyerer, Jürgen

:
Postprint urn:nbn:de:0011-n-3323864 (837 KByte PDF)
MD5 Fingerprint: d42d90e6751385b731e7471b90816597
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 24.3.2015


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014. Proceedings : Columbus, Ohio, USA, 23 - 28 June 2014
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2014
ISBN: 978-147994309-8
pp.209-216
Conference on Computer Vision and Pattern Recognition (CVPR) <2014, Columbus/Ohio>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
In many visual surveillance applications the task of person detection and localization can be solved easier by using thermal long-wave infrared (LWIR) cameras which are less affected by changing illumination or background texture than visual-optical cameras. Especially in outdoor scenes where usually only few hot spots appear in thermal infrared imagery, humans can be detected more reliably due to their prominent infrared signature. We propose a two-stage person recognition approach for LWIR images: (1) the application of Maximally Stable Extremal Regions (MSER) to detect hot spots instead of background subtraction or sliding window and (2) the verification of the detected hot spots using a Discrete Cosine Transform (DCT) based descriptor and a modified Random Naïve Bayes (RNB) classifier. The main contributions are the novel modified RNB classifier and the generality of our method. We achieve high detection rates for several different LWIR datasets with low resolution videos in real-time. While many papers in this topic are dealing with strong constraints such as considering only one dataset, assuming a stationary camera, or detecting only moving persons, we aim at avoiding such constraints to make our approach applicable with moving platforms such as Unmanned Ground Vehicles (UGV).

: http://publica.fraunhofer.de/documents/N-332386.html