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
Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy Image
TL;DR: Quantitative and qualitative analysis show that the proposed hand tracking method is able to predict the hand positions that are closer to the ground truth and the proposed HEI hand representation outperforms other methods in the isolated sign language recognition.
Journal ArticleDOI
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
TL;DR: Zhang et al. as mentioned in this paper proposed a novel RGB-T tracking framework by jointly modeling both appearance and motion cues, and they developed a novel late fusion method to infer the fusion weight maps of both RGB and thermal modalities.
Proceedings ArticleDOI
Estimating SE(3) elements using a dual quaternion based linear Kalman filter.
TL;DR: This work uses a dual quaternion to represent the SE(3) element and uses multiple measurements simultaneously to rewrite the measurement model in a truly linear form with state dependent measurement noise.
Journal ArticleDOI
Fast communication: Adaptive particle sampling and adaptive appearance for multiple video object tracking
Hsu-Yung Cheng,Jenq-Neng Hwang +1 more
TL;DR: An innovative method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking and the concept of adaptive appearance is applied to enhance the robustness of occlusion handling is proposed.
Journal ArticleDOI
Object tracking in videos using adaptive mixture models and active contours
Mohand Saïd Allili,Djemel Ziou +1 more
TL;DR: This paper proposes a novel object tracking algorithm for video sequences, based on matching the object appearance model between successive frames of the sequence using active contours and proposes an adaptive mixture model for the object representation.
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.