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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Posted Content
A methodology for using Kalman filter to determine material parameters from uncertain measurements
TL;DR: In this article, the authors proposed a method to predict the parameters and the errors, required to start Kalman filter based on known parameters that are used to generate the data with different noises used as "measurement data".
Proceedings ArticleDOI
A visual tracker based on improved kernel correlation filter
Liyang Yu,Chunxiao Fan,Yue Ming +2 more
TL;DR: The experimental results on comparison with some state-of-the-art trackers such as Struck, KCF, TLD and MIL demonstrate that the proposed method could deal with the occlusion without any additional complex computing consumption.
ReportDOI
Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression
TL;DR: The Siamese-Dynamic Bayesian Tracking Algorithm (SDBTA) is presented, the first integrated dynamic Bayesian optimization framework in combination with deep learning for video tracking, and a novel data compression algorithm, Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF) which serves as a general purpose, parts-based compression algorithms, applicable to deep model compression.
Proceedings Article
Meanshift algorithm based on kernel bandwidth adaptive adjust
TL;DR: A scheme of kernel bandwidth adaptive adjustment and predictions of object cancroids based on Kalman filter is proposed in this paper and it is demonstrated that this algorithm can realize the kernel bandwidth Adaptive adjustment and object location prediction.
Posted Content
Methods to Quantify Dislocation Behavior with Dark-field X-ray Microscopy Timescans of Single-Crystal Aluminum
TL;DR: In this article, a semi-automated approach is presented to isolate, track, and quantify the behavior of dislocations as composite objects, to include dislocation velocity and orientation in the crystal lattice.
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.