Journal ArticleDOI
Video object tracking using adaptive Kalman filter
Reads0
Chats0
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
More filters
Journal ArticleDOI
An object tracking method using modified galaxy-based search algorithm
TL;DR: A modified GbSA (galaxy based search algorithm) which is more precise than related works and receives current frame and the temporal information related to previous frames as input and tries to find the optimum object state in each one.
Proceedings ArticleDOI
An Efficient Histogram-Based Method for Background Modeling
TL;DR: A simple but efficient modeling scheme, able to deal with static and dynamic backgrounds, is presented, in which each pixel is averaged by a group of dominant bins based on histogram analysis, then the background can be successfully detected and further classified the background into static or dynamic according to the statistic properties.
Proceedings ArticleDOI
A model for honey bee tracking on 2D video
Cheng Yang,John Collins +1 more
TL;DR: This paper introduces a model to track bees on 2D video with a 50Hz frame rate, which detects bees using background subtraction and colour thresholding, producing a binary image showing the bees as blobs.
Journal ArticleDOI
Combination of Robust Algorithm and Head-Tracking for a Feedforward Active Headrest
TL;DR: In this article, a robust algorithm based on the feed forward active noise control is proposed to improve the noise control performance during head rotations, and head tracking system with infrared rangefinders tracks the head position based on Kalman filter to further improve system performance with head movements.
Journal ArticleDOI
KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering
TL;DR: An efficient video optical flow estimation method by exploiting the temporal coherence and context dynamics under a Kalman filtering system that performs favorably against the state-of-the-art approaches.
References
More filters
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.