Detecting Multiple Objects under Partial Occlusion by Integrating Classification and Tracking Approaches

摘要

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.