Improvement of Human Tracking in Stereoscopic Environment Using Subtraction Stereo with Shadow Detection

Abstract

The combination of subtraction stereo with shadow detection we propose improves people tracking in stereoscopic environments. Subtraction stereo is a stereo matching method which is fast and robust for the correspondence problem – one of the most serious issues in computer vision – restricting the search range of matching to foreground regions. Shadow detection gives adequate foreground regions of tracked people by removing cast shadows. This leads to accurate three-dimensional measurement of positions in stereoscopic environment tracking. By focusing on disparity images obtained by subtraction stereo, we can detect people easily based on standard labeling. Objects can also be measured directly in size by subtraction stereo without geometric information about environments for tracking. This is important for installing the tracking system easily. To track multiple passing people, we use the extended Kalman filter to address the occlusion problem usually encountered in crowded environments. The proposed method is verified by experiments using unknown stereoscopic environments.