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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Journal ArticleDOI
Spatial-aware correlation filters with adaptive weight maps for visual tracking
Feng Tang,Qiang Ling +1 more
TL;DR: A spatial-aware adaptive weight map is adaptively generated by combining the target likelihood, which quantitatively measures whether a pixel belongs to the target or the background, and prior spatial weights of pixels, which can effectively avoid the tracking filter corruption problem when the target is severely occluded and the target search area is mainly filled with pixels of the background.
Journal ArticleDOI
A secure and robust video steganography scheme for covert communication in H.264/AVC
Mukesh Dalal,Mamta Juneja +1 more
TL;DR: In this article, the authors employed Discrete Wavelet Transform (DWT) on the Region of Interest (ROI) based on multiple moving objects tracking to improve the capacity of H.264/AVC video format.
DissertationDOI
Experimental Modelling of Debris Dynamics in Tsunami-Like Flow Conditions
TL;DR: In this article, the authors present a Table of Table of contents of the paper "Acknowledgements and acknowledgements of the authors of this paper: https://www.goprocessor.org/
Journal ArticleDOI
A Multisource Heterogeneous Data Fusion Method for Pedestrian Tracking
TL;DR: A pedestrian tracking method for fusing multisource heterogeneous sensing information, including video, RGB-D sequences, and inertial sensor data, which outperforms the existing tracking method and is robust to target occlusion, illumination changes, and interference from similar textures or complex backgrounds.
Book ChapterDOI
Tracking Model for Abnormal Behavior from Multiple Network CCTV Using the Kalman Filter
Yongik Yoon,Jee-Ae Chun +1 more
TL;DR: This paper proposes a tracking model for prevention of crime by using Kalman Filter that consists of three steps as follows; object assessment, situation assessment, and risk assessment.
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.