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Kernel-based method for tracking objects with rotation and translation

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TLDR
This paper addresses the issue of tracking translation and rotation simultaneously by starting with a kernel-based spatial-spectral model for object representation, and derive a new formulation to the tracking of translational and rotational object.
Abstract
This paper addresses the issue of tracking translation and rotation simultaneously. Starting with a kernel-based spatial-spectral model for object representation, we define an l/sub 2/-norm similarity measure between the target object and the observation, and derive a new formulation to the tracking of translational and rotational object. Based on the tracking formulation, an iterative procedure is proposed. We also develop an adaptive kernel model to cope with varying appearance. Experimental results are presented for both synthetic data and real-world traffic video.

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Journal ArticleDOI

A spatial-color mean-shift object tracking algorithm with scale and orientation estimation

TL;DR: An enhanced mean-shift tracking algorithm using joint spatial-color feature and a novel similarity measure function is proposed and the experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale changes.
Proceedings ArticleDOI

Tunable Kernels for Tracking

TL;DR: A tunable representation for tracking that simultaneously encodes appearance and geometry in a manner that enables the use of mean-shift iterations for tracking and yet retains robustness to spatial deformations and feature density variations.
Journal ArticleDOI

Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking

TL;DR: A novel visual object tracking scheme is proposed by using joint point feature correspondences and object appearance similarity that has yielded a marked improvement in terms of tracking drift, tightness, and accuracy of tracked bounding boxes, especially for complex video scenarios containing long-term partial occlusions or intersections, deformation, or background clutter.
Proceedings ArticleDOI

Kernel-based head tracker for videophony

TL;DR: An approach for automatically segmenting and tracking a face in a sequence of color images is presented, using a geometric transformation enabling the rotation, scaling, and translation of the target, and an affine illumination change model.
Proceedings ArticleDOI

A new spatial-color mean-shift object tracking algorithm with scale and orientation estimation

TL;DR: The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale.
References
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Journal ArticleDOI

Pfinder: real-time tracking of the human body

TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Journal ArticleDOI

Detecting faces in images: a survey

TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Proceedings ArticleDOI

Real-time tracking of non-rigid objects using mean shift

TL;DR: The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution for real time tracking of non-rigid objects seen from a moving camera.
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