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.
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Palaio, H., Batista, J. (2009). Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_11
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DOI: https://doi.org/10.1007/978-3-642-02172-5_11
Publisher Name: Springer, Berlin, Heidelberg
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