Irfan Essa’s Academic Activities

Tracking multiple objects through occlusions (IEEEXplore#)

Huang, Y. Essa, I.
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
This paper appears in: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
Publication Date: 20-25 June 2005
Volume: 2
On page(s): 1051 - 1058 vol. 2
Number of Pages: 2 vol. (xxxvii 1216)
ISSN: 1063-6919
ISBN: 0-7695-2372-2
INSPEC Accession Number:8633324
Digital Object Identifier: 10.1109/CVPR.2005.350
Posted online: 2005-07-25 08:18:55.0

Abstract

We present an approach for tracking varying number of objects through both temporally and spatially significant occlusions. Our method builds on the idea of object permanence to reason about occlusions. To this end, tracking is performed at both the region level and the object level. At the region level, a customized genetic algorithm is used to search for optimal region tracks. This 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. The proposed architecture is capable of tracking objects even in the presence of long periods of full occlusions. We demonstrate the viability of this approach by experimenting on several videos of a user interacting with a variety of objects on a desktop.

Propagation networks for recognition of partially ordered sequential action (IEEEXplore)

Yifan Shi Yan Huang Minnen, D. Bobick, A. Essa, I.
GVU Center, Georgia Inst. of Technol., Atlanta, GA, USA
This paper appears in: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
Publication Date: 27 June-2 July 2004
Volume: 2
On page(s): II-862 - II-869 Vol.2
Number of Pages: 2001
ISSN: 1063-6919
ISBN: 0-7695-2158-4
INSPEC Accession Number:8161557
Digital Object Identifier: 10.1109/CVPR.2004.1315255
Posted online: 2004-07-19 11:09:30.0

Abstract

We present propagation networks (P-nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activity using partially ordered intervals. Each interval is restricted by both temporal and logical constraints, including information about its duration and its temporal relationship with other intervals. P-nets associate one node with each temporal interval. Each node is triggered according to a probability density function that depends on the state of its parent nodes. Each node also has an associated observation function that characterizes supporting perceptual evidence. To facilitate real-time analysis, we introduce a particle filter framework to explore the conditional state space. We modify the original condensation algorithm to more efficiently sample a discrete state space (D-condensation). Experiments in the domain of blood glucose monitor calibration demonstrate both the representational power of P-nets and the effectiveness of the D-condensation algorithm.