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
Dengue larvae detection and tracking using CNN and kalman filtering
TL;DR: Li et al. as discussed by the authors proposed an automated model for dengue larvae detection and tracking using Convolutional Neural Network (CNN) and Kalman filters, which showed excellent results considering the small size of larvae and the challenging dataset.
Journal ArticleDOI
CLSTM-KF reconstruction method for a low-activity moving radiation source localization and tracking with a coded-aperture gamma camera
TL;DR: The CLSTM-KF method provides a better choice than the traditional methods in locating and tracking a low-activity moving radiation source and the computation time can also meet the application requirements.
Journal ArticleDOI
Controlling Surveillance Systems and PTZ Cameras from a Mobile Device
TL;DR: An Android application has been developed that can be used on smart phones to monitor and follow the performance of the data center camera monitoring system, and to view a report of faults and maintenance to ensure the efficiency of the system.
Journal ArticleDOI
Improved Algorithm for Object Tracking in Video Camera Network
Mukesh Tiwari,Rakesh Singhai +1 more
TL;DR: The proposed algorithm can easily detect object even if it is either hided or it moves to the frame of camera 2, or any other camera installed within the same camera network.
Book ChapterDOI
Formalization of People and Crowd Detection and Tracking for Smart Video Surveillance
TL;DR: In this article , the authors presented a formalization of the problem of detection and tracking of people and crowd in video sequences and developed algorithms for detecting and tracking people and groups of people in indoor and outdoor environments.
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.