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
Book ChapterDOI
Depth-Adaptive Computational Policies for Efficient Visual Tracking
Chris Ying,Katerina Fragkiadaki +1 more
TL;DR: A depth-adaptive convolutional Siamese network that performs video tracking adaptively at multiple neural network depths that achieves accuracy comparable to the state-of-the-art on the VOT2016 benchmark and achieves higher accuracy for a given computational cost than traditional fixed-structure neural networks.
Dissertation
The Use of Video to Detect and Measure Pollen on Bees Entering a Hive
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
Journal ArticleDOI
Visual Tracking Based on Complementary Learners with Distractor Handling
TL;DR: In this study, first the distractor must be detected by learning the responses from the color-histogram- and correlation-filter-based representation, and then the target location is determined by deciding whether the responds from each representation should be merged or only the response from the correlation filter should be used.
Journal ArticleDOI
Stochastic reconstruction of filament paths in fibre bundles based on two-dimensional input data
TL;DR: In this paper, a probabilistic method was proposed to reconstruct paths of individual filaments from a series of two-dimensional micrographs, which allowed three-dimensional filament arrangements to be reconstructed and the effects of local arrangements on properties to be quantified.
Journal ArticleDOI
Combined Kalman Filter and Multifeature Fusion Siamese Network for Real-Time Visual Tracking
Lijun Zhou,Jianlin Zhang +1 more
TL;DR: The introduction of the Kalman filter compensates for the shortcomings that SiamFC can only track offline, and the tracking network has an online learning process and the fusion of multiresolution features to obtain multiple response scores map helps the tracker to obtain robust features that can be adapted to a variety of tracking targets.
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.