Readings in nonmonotonic reasoning
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: A Real Time System for Detecting and Tracking People
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Multivalued default logic for identity maintenance in visual surveillance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Multimedia Data Engineering & Management
| Hi-index | 0.00 |
Persistent tracking systems require the capacity to track individuals by maintaining identity across visibility gaps caused by occlusion events. In traditional computer vision systems, the flow of information is typically bottom-up. The low level image processing modules take video input, perform early vision tasks such as background subtraction and object detection,and pass this information to the high level reasoning module. This paper describes the architecture of a system that uses top-down information flow to perform identity maintenance across occlusion events. This system uses the high level reasoning module to provide control feedback to the low level image processing module to perform forensic analysis of archival video and actively acquire information required to arrive at identity decisions. This functionality is in addition to traditional bottom-up reasoning about identity, employing contextual cues and appearance matching, within the multivalued default logic framework proposed in [18]. This framework, in addition to bestowing upon the system the property of nonmonotonicity, also allows for it to qualitatively encode its confidence in the identity decisions it takes.