Fundamental Frequency Gabor Filters for Object Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
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
Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Gabor parameter selection for local feature detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Hi-index | 0.00 |
This paper presents an approach to label and track multiple objects through both temporally and spatially significant occlusions. 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 o search for optimal region tracks which limits the scope of object trajectories. 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. Results from experiments with real video data show the effectiveness of the approach hereby proposed.