Moving Object Recognition and Classification based on Recursive Shape Parameter Estimation

Dieter Koller

In Proc. of the 12th Israeli Conf. on Artificial Intelligence, Computer Vision, and Neural Networks, pp. 359-368, Tel-Aviv, Israel, December 27-28, 1993.

Abstract

We present an approach for recognizing and classifying moving vehicles in monocular images sequences of traffic scenes recorded by a stationary camera. A generic vehicle model, represented by a 3D polyhedral model described by 12 length parameters, is used to cover the different shapes of road vehicles. The object recognition process is initialized by formulating a model hypothesis using a reference model and initial values provided by a motion segmentation step from a model-based tracking system described previously. This model hypothesis is verified and the shape as well as the pose and motion parameters of the object are estimated simultaneously. A recursive estimator updates the state description of the shape and motion parameters. In this way all relevant data from the image sequence evaluated so far are accumulated and used for the shape parameter estimation and classification of a moving vehicle. A classification is based on the assumption that differences between class members can be considered as deformations of the shape of a stored prototype. Results on real world traffic scenes are presented and some open problems are outlined.

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Last modified on Tuesday, November 20, 1996, Dieter Koller (koller@vision.caltech.edu)