A Mobile Vision System for Urban Object Detection with Informative Local Descriptors
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 [7] 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 ? 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 - important for mobile vision - runtimes which are
? 8 (?24) times faster for the MPG-20 (ZuBuD) database.