Výsledky vyhledávání - "Katharina Quast"

AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios

Autor: Andre Kaup, Katharina Quast

Publikováno v: EURASIP Journal on Image and Video Processing, Vol 2011 (2011)

A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9263653c755a47798f5a627d62ae59e4
https://doi.org/10.1155/2011/814285

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Boosting based object detection using a geometric model

Autor: Andre Kaup, Mohan M. Trivedi, Katharina Quast, Christoph Seeger

Publikováno v: ICIP

In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d7805df04e5a5875105d430938214d3f
https://doi.org/10.1109/icip.2011.6116487

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Spatial Scalable Region of Interest Transcoding of JPEG2000 for Video Surveillance

Autor: Andre Kaup, Katharina Quast

Publikováno v: AVSS

A new method for spatial scalable region of interest transcoding of JPEG2000 images for video surveillance applications is proposed in this paper. The method transcodes HD images into quarter HD images with a ROI in HD resolution. The transcoding met

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5d8be5959524f0bda61e04731bdb2656
https://doi.org/10.1109/avss.2008.17

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Shape adaptive mean shift object tracking using Gaussian mixture models

Autor: Katharina Quast, Andree Kaup

Publikováno v: Scopus-Elsevier
Lecture Notes in Electrical Engineering ISBN: 9781461438304
WIAMIS

GMM-SAMT, a new object tracking algorithm based on a combination of the mean shift principal and Gaussian mixture models is presented. GMM-SAMT uses an asymmetric shape adapted kernel, instead of a symmetrical one like in traditional mean shift track

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c8d1cf23a4ac28084e70d53ad345289
http://www.scopus.com/inward/record.url?eid=2-s2.0-78649924922&partnerID=MN8TOARS