The application of intrinsic variable preserving manifold learning method to tracking multiple people with occlusion reasoning

Abstract

Tracking multiple people in crowded and cluttered dynamic scenes is a very difficult task in robotic vision due to the highly frequent occlusion and lack of visibility of objects. In this paper, we present a manifold learning based multiple people tracking approach with occlusion reasoning to solve this problem. In our previous work, a new Intrinsic Variable Preserving Manifold Learning (IVPML) method is proposed, by which the continuity of the intrinsic motion variables for tracking is preserved on a new manifold after dimensionality reduction. In this paper, the IVPML method is extended to be applied to tracking multiple people with occlusion situations. Associated with spatio-temporal continuity of tracking and IVPML method, a novel robust occlusion reasoning method is proposed during the alternations of multiple people. For occlusion recovery, region covariance representation including both spatial and statistic properties of objects are used to detect people after occlusion. The multiple people tracking method has been successfully applied to mobile robotic visual tracking system in several complicated environments. Comparisons and experimental results have shown the effectiveness of the new algorithm in various situations.