Journal ArticleDOI
Video object tracking using adaptive Kalman filter
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TLDR
The proposed method has the robust ability to track theMoving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the movingobject, and changing the velocity of moving object suddenly.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2006-12-01. It has received 314 citations till now. The article focuses on the topics: Video tracking & Kalman filter.read more
Citations
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Book ChapterDOI
Motion-Augmented Inference and Joint Kernels in Structured Learning for Object Tracking
Kumara Ratnayake,Maria A. Amer +1 more
TL;DR: This thesis explores improving object tracking by employing advanced techniques in machine learning theory to account for intrinsic changes in the object appearance under those challenging conditions, and proposes a fast and competitive method by modeling target dynamics as a random stochastic process, and using structured support vector machines.
A robotic camera platform for evaluation of biomimetic gaze stabilization using adaptive cerebellar feedback
TL;DR: This thesis describes the development of a robotic platform for evaluation of gaze stabilization algorithms built for the Sensorimotor Systems Laboratory at the University of British Columbia.
Journal ArticleDOI
Fast drawing of traffic sign using mobile mapping system
TL;DR: A drawing strategy is proposed to quickly approximate the boundary of traffic sign and can detect traffic signs at the rate of over 80% in around 10 milliseconds, promising for the large-scale traffic sign survey and change detection using the mobile mapping system.
Journal ArticleDOI
Probabilistic color matching and tracking of human subjects
TL;DR: This work applies the CM approach toward the tracking of human subjects in real time by matching and tracking the underlying color pattern as observed from a fixed camera, and shows that there is an optimum alphabet size and segmentation of the RGB color cube for efficient tracking.
Journal ArticleDOI
Perspective model-based visual tracking scheme for robust tracking of objects in complex environs
Emmanuel Joy,J. Dinesh Peter +1 more
TL;DR: Experimental results of the proposed method, when tested on a number of challenging datasets with occlusion, non-rigid deformation, and other major challenges highlights the better ability and robustness ofThe proposed method under tough conditions.
References
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BookDOI
An Introduction to the Kalman Filter
Greg Welch,Gary Bishop +1 more
TL;DR: The discrete Kalman filter as mentioned in this paper is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error.
Journal ArticleDOI
A Survey of Computer Vision-Based Human Motion Capture
Thomas B. Moeslund,Erik Granum +1 more
TL;DR: A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented, with a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition.
Proceedings ArticleDOI
Moving target classification and tracking from real-time video
TL;DR: An end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to image-based properties, and then robustly tracking them is described.
Journal ArticleDOI
Robust online appearance models for visual tracking
TL;DR: A framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects to provide robustness in the face of image outliers, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.
Book ChapterDOI
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
TL;DR: A probabilistic method for tracking 3D articulated human figures in monocular image sequences that relies only on a frame-to-frame assumption of brightness constancy and hence is able to track people under changing viewpoints, in grayscale image sequences, and with complex unknown backgrounds.