The adaptive pyramid: a framework for 2D image analysis
CVGIP: Image Understanding
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Background Layer Model for Object Tracking Through Occlusion
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment
Presence: Teleoperators and Virtual Environments
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Spatio-Temporal Scene Analysis Based on Graph Algorithms to Determine Rigid and Articulated Objects
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Supporting the design process with hypergraph genetic operators
Advanced Engineering Informatics
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In a video surveillance system the object tracking is one of the most challenging problem. In fact objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. In this paper we present a novel method of tracking objects through occlusions using a multi-resolution representation of the moving regions. The matching between objects in two consecutive frames to recognize the trajectories is preformed in a graph theoretic approach. The experimental results on the standard database PEST2001 show that the approach looks promising.