Detecting multiple objects under partial occlusion by integrating classification and tracking approaches

Abstract

A visual-based framework for detecting in real time multiple objects in real outdoor scenes is presented. The main novelty of the system is its capability to reduce the problems of partial occlusions and/or overlaps that occur very commonly in real scenes containing multiple moving objects. Overlaps and occlusions are dealt with by integrating classification and tracking procedures into a data-fusion distributed sensory network. Neural tree-based networks are applied to distinguish among isolated objects and groups of objects on the image plane. Extended Kalman filters are applied to estimate the number of objects in the scene, their position, and the related motion parameters. Experimental results on complex outdoor scenes with multiple moving objects are presented. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 263–276, 2000