Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition
Learning an intrinsic-variable preserving manifold for dynamic visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Tracking feature extraction based on manifold learning framework
Journal of Experimental & Theoretical Artificial Intelligence - Advances in knowledge discovery and data analysis for artificial intelligence
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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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.