Visual tracking and learning using speeded up robust features

Authors:
Jingyu Li;Yulei Wang;Yanjie Wang
Affiliations:
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China and Graduate University of Chinese Academy of Sciences, Beijing 100039, China and In ...;Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Duisburg 47057, Germany;Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Venue:
Pattern Recognition Letters
Year:
2012

Citing 15
Cited 0

The image flow constraint equation

Computer Vision, Graphics, and Image Processing
An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Exclusion Principle for Tracking Multiple Objects

International Journal of Computer Vision
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

International Journal of Computer Vision
Contour Tracking by Stochastic Propagation of Conditional Density

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Lucas-Kanade 20 Years On: A Unifying Framework

International Journal of Computer Vision
The Template Update Problem

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
Multi-dimensional visual tracking using scatter search particle filter

Pattern Recognition Letters
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Object tracking using SIFT features and mean shift

Computer Vision and Image Understanding
Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection

Machine Vision and Applications
Target Tracking Using a Joint Acoustic Video System

IEEE Transactions on Multimedia
A survey on visual surveillance of object motion and behaviors

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.10

Visualization

Abstract

A speeded up robust features (SURF) based optical flow algorithm is presented for visual tracking in real scenarios. SURF construct invariant features to correspond the blobs of interest across frames. Meanwhile, new feature-based optical flow algorithm is used to compute the warp matrix of a region centered on SURF key points. Furthermore, on-line visual learning for long-term tracking is performed using incremental object subspace method, which includes the correct update of the sample mean and appearance model. The proposed SURF based tracking and learning method contributes measurably to improving overall tracking performance. Experimental work demonstrates that the proposed strategy improves the performance of the classical optical flow algorithms in complicated real scenarios.