A Mobile Vision System for Multimedia Trouist Applications in Urban Environment
Publikation aus Digital
Paletta L., Fritz G., Seifert C., Luley P., Almer A.
Proc IEEE Intelligent Transportation System Conference (ITSC2006),Toronto, Canada , 2006
We present a computer vision system for the detection and identification
of urban objects from mobile phone imagery, e.g., for the application
of tourist information services. Recognition is based on MAP decision
making over weak object hypotheses from local descriptor responses
in the mobile imagery. We present an improvement over the standard
SIFT key detector (Lowe, 2004) by selecting only informative (i-SIFT)
keys for descriptor matching. Selection is applied first to reduce
the complexity of the object model and second to accelerate detection
by selective filtering. We present results on the MPG-20 mobile phone
imagery with severe illumination, scale and viewpoint changes in
the images, performing with p ap 98% accuracy in identification,
efficient (100%) background rejection, efficient (0%) false alarm
rate, and reliable quality of service under extreme illumination
conditions, significantly improving standard SIFT based recognition
in every sense, providing mportant for mobile vision - runtimes which
are ap 8 (ap 24) times faster for the MPG-20 (ZuBuD) database.