Fuzzy Feature-Based Upper Body Tracking with IP PTZ Camera Control

Authors:
Parisa Darvish Zadeh Varcheie;Guillaume-Alexandre Bilodeau
Affiliations:
Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Canada H3C 3A7;Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Canada H3C 3A7
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 8
Cited 0

Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes

International Journal of Computer Vision
A survey of skin-color modeling and detection methods

Pattern Recognition
Accurate object contour tracking based on boundary edge selection

Pattern Recognition
Bittracker—A Bitmap Tracker for Visual Tracking under Very General Conditions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Life Spans

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical face tracking by using PTZ camera

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Tracking multiple people in the context of video surveillance

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a fuzzy-feature based method for online upper body tracking using an IP PTZ camera. Because the camera uses a built-in web server, camera control entails camera response time and network delays, and thus, the frame rate is irregular and in general low (2-7 fps). It detects at every frame, candidate targets by extracting motion, a sampling method, and appearance. The target is detected among samples with a fuzzy classifier. Results show that our system has a good target detection precision ( 85%), low track fragmentation, and the target is almost always localized within 1/6th of the image diagonal from the image center.