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» Multiple Object Tracking with Kernel Particle Filter
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AVSS
2006
IEEE
13 years 11 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
BMVC
2010
13 years 5 months ago
On-line Adaption of Class-specific Codebooks for Instance Tracking
Off-line trained class-specific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, o...
Juergen Gall, Nima Razavi, Luc J. Van Gool
VIP
2003
13 years 9 months ago
Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces
The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face trackin...
John G. Allen, Richard Y. D. Xu, Jesse S. Jin
JUCS
2010
265views more  JUCS 2010»
13 years 6 months ago
A General Framework for Multi-Human Tracking using Kalman Filter and Fast Mean Shift Algorithms
: The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human track...
Ahmed Ali, Kenji Terada
IROS
2009
IEEE
304views Robotics» more  IROS 2009»
14 years 2 months ago
Real time tracking using an active pan-tilt-zoom network camera
— We present here a real time active vision system on a PTZ network camera to track an object of interest. We address two critical issues in this paper. One is the control of the...
Thang Ba Dinh, Qian Yu, Gérard G. Medioni