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A New Approach to Tracking 3D Objects in 2D Image Sequences

Conference Paper Perception Artificial Intelligence

Abstract

We present a new technique for tracking 3D objects from 2D image sequences through the integration of qualitative and quantitative techniques. The deformable models are initialized based on a previously developed part-based qualitative shape segmentation system. Using a physics-based quantitative approach, objects are subsequently tracked without feature correspondence based on generalized forces computed from the stereo images. The automatic prediction of possible edge occlusion and disocclusion is performed using an extended Kalman filter. To cope with possible occlusion caused by a previously undetected object, we monitor the magnitude and direction of the computed image forces exerted on the models. Abrupt changes to these forces trigger scene re-segmentation and model re-initialization through the qualitative shape segmentation system. Tracking is subsequently continued using only local image forces. We demonstrate our technique in experiments involving image sequences from complex motions of 3D objects.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
751805097323110590
v2026.09.13