CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibrating pan-tilt cameras in wide-area surveillance networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Uncalibrated Framework for On-line Camera Cooperation to Acquire Human Head Imagery in Wide Areas
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
A dataset and evaluation methodology for template-based tracking algorithms
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
People tracking using a network-based PTZ camera
Machine Vision and Applications
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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PTZ (Pan-Tilt-Zoom) cameras are powerful devices in video surveillance applications, because they offer both wide area coverage and highly detailed images in a single device. Tracking with a PTZ camera is a closed loop procedure that involves computer vision algorithms and control strategies, both crucial in developing an effective working system. In this work, we propose a novel experimental framework that allows to evaluate image tracking algorithms in controlled and repeatable scenarios, combining the PTZ camera with a calibrated projector screen on which we can play different tracking situations. We applied such setup to compare two different tracking algorithms, a kernel-based (mean-shift) tracking and a particle filter, opportunely tuned to fit with a PTZ camera. As shown in the experiments, our system allows to finely investigate pros and cons of each algorithm.