Local Moving Object Shape Priors
Detecting, isolating, and tracking moving objects in an outdoor scene is a
fundamental problem of visual surveillance. A key component of most
approaches to this problem is the construction of a background model of
intensity values. We propose extending background modeling to include
learning a model of the expected shape of foreground objects. This paper
describes our approach to shape description, shape space density estimation,
and unsupervised model training. A key contribution is a description of
properties of the joint distribution of object shape and image location. We
show object segmentation and anomalous shape detection results on video
captured from road intersections. Our results demonstrate the usefulness of
building scene-specific and spatially-localized shape background models.
First a large database of examples shapes are collected from the scene, the
process is described in the figure below. Then a PCA basis is built from this
database. This basis is then used to improve object segmentation and anomaly
An overview of the process we use to collect example shapes.
This figure (bottom) shows the coordinates of example
shapes with respect to a PCA subspace (top). The first principal direction,
left, corresponds to size of the object. The second principal direction, right,
corresponds to the orientation of the object.
Example shapes and their position in the first two dimensions of a local PCA
subspace. Points are colored by when they arrived in the scene. Notice that two
different buses moved through the scene.