We present a system for scale and affine invariant recognition of vehicular objects in video sequences. We use
local descriptors (SIFT keypoints) from image frames to model the object. These features are claimed in the
literature to be highly distinctive and invariant to rotation, scale, and affine transformations. However, since the
SIFT keypoints that are extracted from an object are instance-specific (variable), they form a dynamic feature
space. This presents certain challenges for classification techniques, which generally require use of the same set
of features for every instance of an object to be classified. To resolve this difficulty, we associate the extracted
keypoints to the components (representative keypoints) in a mixture model for each target class. While the
extracted keypoints are variable, the mixture components are fixed. The mixture models the keypoint features,
as well as the location and scale at which each keypoint was detected in the frame. Keypoint to component
association is achieved via a switching optimization procedure that locally maximizes the joint likelihood of
keypoints and their locations and scales with the latter based on an affine transformation. To each mixture
component from a class, we link a (first layer) support vector machine (SVM) classifier which votes for or
against the hypothesis that the keypoint associated to the component belongs to the model's target class. A
second layer SVM pools the votes from the ensemble of SVM classifiers in the first layer and gives the final
class decision. We show promising results of experiments for video sequences from the VIVID database.
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