W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of visual information retrieval
Principles of visual information retrieval
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
Integrated Region- and Pixel-based Approach to Background Modelling
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Multi-resolution background modeling of dynamic scenes using weighted match filters
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Kernel-Based Bayesian Filtering for Object Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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Currently there are many systems available that use relevance feedback for text and image retrieval. This query by example method has been shown to optimize the search strategy whilst keeping a fast response time, two important factors when querying large image databases. The use of relevance feedback in real time video object tracking and identification however is essentially unexplored. This paper discusses the ongoing project and current results towards integrating interactive relevance feedback within the context of video object tracking. We discuss the important limitations in real time video object tracking and we design a next generation video object tracking system which exploits interactive relevance feedback towards addressing the primary limitations.