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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Proceedings ArticleDOI
Performance of invariant feature descriptors with adaptive prediction in occlusion handling
TL;DR: An improved performance of invariant feature descriptors in occlusion handling by using adaptive prediction from Kalman filter using auto-tuned error covariance parameters based on the changing conditions of the feature descriptor in a tracked object.
Proceedings ArticleDOI
3D object tracking using three Kalman filters
Yasir Salih,Aamir Saeed Malik +1 more
TL;DR: This paper provides a detailed evaluation of the most common Kalman filters, their use in the literature and their implementation for 3D visual tracking.
Journal ArticleDOI
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
TL;DR: Hu et al. as mentioned in this paper built a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking and introduced a motion modeling baseline to improve the detection rate and reduce false alarms.
Proceedings ArticleDOI
Multiple object tracking using HSV color space
TL;DR: This paper proposes a multiple object strategy along with a failure recovery mechanism and uses PETS video sequences to evaluate the performance of the proposed method and compare it with some of the recently proposed method.
Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications
TL;DR: By developing black box Variational inference, this thesis has opened doors to new models, better posterior approximations, and new varieties of variational inference algorithms.
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.