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
Robust object tracking with crow search optimized multi-cue particle filter
TL;DR: A novel resampling method based upon crow search optimization to overcome low performing particles detected as the outlier and an adaptive fusion model to integrate multi-cue extracted for each evaluated particle.
Journal ArticleDOI
Fuzzy Particle Filter for Video Surveillance
Vinu Thomas,Ajoy Kumar Ray +1 more
TL;DR: The proposed algorithm successfully tracks multiple objects by the use of an adaptive Gaussian mixture model for background modeling and a sequential Monte-Carlo-based tracking algorithm.
Journal ArticleDOI
Noncontact spirometry with a webcam.
TL;DR: Compared to the traditional spirometers, the present noncontact spirometry does not require using a spirometer, breathing into a mouthpiece, or wearing a nose clip, thus making spirometry test more easily accessible for the growing population of asthma and chronic obstructive pulmonary diseases.
Proceedings ArticleDOI
A robust object tracking algorithm based on SURF
TL;DR: A robust object tracking algorithm based on SURF is proposed which uses a two-stage matching method to improve the accuracy of SURF matching points and uses matching points between new template and candidate region to locate the initial position of object.
Journal ArticleDOI
Incremental multi-class semi-supervised clustering regularized by Kalman filtering
TL;DR: It is shown how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm.
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.