Appearance modeling under geometric context for object recognition in videos
Appearance modeling under geometric context for object recognition in videos
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Date
2006-08-03
Authors
Li, Jian
Advisor
Chellappa, Rama
Citation
DRUM DOI
Abstract
Object recognition is a very important high-level task in
surveillance applications. This dissertation focuses on building
appearance models for object recognition and exploring the
relationship between shape and appearance for two key types of
objects, human and vehicle. The dissertation proposes a generic
framework that models the appearance while incorporating certain
geometric prior information, or the so-called geometric
context. Then under this framework, special methods are developed
for recognizing humans and vehicles based on their appearance and
shape attributes in surveillance videos.
The first part of the dissertation presents a unified framework
based on a general definition of geometric transform (GeT) which is
applied to modeling object appearances under geometric context. The
GeT models the appearance by applying designed functionals over
certain geometric sets. GeT unifies Radon transform, trace
transform, image warping etc. Moreover, five novel types of GeTs are
introduced and applied to fingerprinting the appearance inside a
contour. They include GeT based on level sets, GeT based on shape
matching, GeT based on feature curves, GeT invariant to occlusion,
and a multi-resolution GeT (MRGeT) that combines both shape and
appearance information.
The second part focuses on how to use the GeT to build appearance
models for objects like walking humans, which have articulated
motion of body parts. This part also illustrates the application of
GeT for object recognition, image segmentation, video retrieval, and
image synthesis. The proposed approach produces promising results
when applied to automatic body part segmentation and fingerprinting
the appearance of a human and body parts despite the presence of
non-rigid deformations and articulated motion.
It is very important to understand the 3D structure of vehicles in
order to recognize them. To reconstruct the 3D model of a vehicle,
the third part presents a factorization method for structure from
planar motion. Experimental results show that the algorithm
is accurate and fairly robust to noise and inaccurate calibration.
Differences and the dual relationship between planar motion and
planar object are also clarified in this part. Based on our method,
a fully automated vehicle reconstruction system has been designed.