Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix

Authors:
Hélio Palaio;Jorge Batista
Affiliations:
ISR-Institute of Systems and Robotics DEEC-FCTUC, University of Coimbra, Portugal;ISR-Institute of Systems and Robotics DEEC-FCTUC, University of Coimbra, Portugal
Venue:
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Year:
2009

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Abstract

This paper presents an approach to label and track multiple objects through both temporally and spatially significant occlusions. The proposed method builds on the idea of object permanence to reason about occlusion. To this end, tracking is performed at both the region level and the object level. At the region level, a kernel based particle filter method is used to search for optimal region tracks. At the object level, each object is located based on adaptive appearance models, spatial distributions and inter-occlusion relationships. Region covariance matrices are used to model objects appearance. We analyzed the advantages of using Gabor functions as features and embedded them in the RCMs to get a more accurate descriptor. The proposed architecture is capable of tracking multiple objects even in the presence of periods of full occlusions.