scholarly journals A multi-feature tracking algorithm enabling adaptation to context variations

2011 ◽  
Author(s):  
D.P. Chau ◽  
F. Bremond ◽  
M. Thonnat
Keyword(s):  
Feature Tracking ◽  
Tracking Algorithm

scholarly journals Feature Tracking Algorithm for Circumferential Strain using High Frame Rate Echocardiography

2016 ◽  
Author(s):  
Martin Vandborg Andersen ◽  
Cooper Moore ◽  
Samuel Schmidt ◽  
Peter Sogaard ◽  
Johannes Struijk ◽  
...  
Keyword(s):  
Feature Tracking ◽  
Circumferential Strain ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
High Frame Rate

A Robust and Efficient Facial Feature Tracking Algorithm

2012 ◽  
Vol 38 (5) ◽  
pp. 788-796 ◽  
Author(s):  
Chen HUANG ◽  
Xiao-Qing DING ◽  
Chi FANG
Keyword(s):  
Feature Tracking ◽  
Facial Feature ◽  
Tracking Algorithm ◽  
Facial Feature Tracking

scholarly journals A 4D feature‐tracking algorithm: A multidimensional view of cyclone systems

2019 ◽  
Vol 145 (719) ◽  
pp. 395-417 ◽  
Author(s):  
Susan Gabriela Lakkis ◽  
Pablo Canziani ◽  
Adrián Yuchechen ◽  
Leandro Rocamora ◽  
Agustin Caferri ◽  
...  
Keyword(s):  
Feature Tracking ◽  
Tracking Algorithm

scholarly journals Open-source feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery

2016 ◽  
Vol 10 (2) ◽  
pp. 913-925 ◽  
Author(s):  
Stefan Muckenhuber ◽  
Anton Andreevich Korosov ◽  
Stein Sandven
Keyword(s):  
Sea Ice ◽  
Open Source ◽  
Feature Tracking ◽  
Tracking Algorithm ◽  
Test Image ◽  
Speeded Up Robust Features ◽  
Radar Images ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Image Pairs

Abstract. A computationally efficient, open-source feature-tracking algorithm, called ORB, is adopted and tuned for sea ice drift retrieval from Sentinel-1 SAR (Synthetic Aperture Radar) images. The most suitable setting and parameter values have been found using four Sentinel-1 image pairs representative of sea ice conditions between Greenland and Severnaya Zemlya during winter and spring. The performance of the algorithm is compared to two other feature-tracking algorithms, namely SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). Having been applied to 43 test image pairs acquired over Fram Strait and the north-east of Greenland, the tuned ORB (Oriented FAST and Rotated BRIEF) algorithm produces the highest number of vectors (177 513, SIFT: 43 260 and SURF: 25 113), while being computationally most efficient (66 s, SIFT: 182 s and SURF: 99 s per image pair using a 2.7 GHz processor with 8 GB memory). For validation purposes, 314 manually drawn vectors have been compared with the closest calculated vectors, and the resulting root mean square error of ice drift is 563 m. All test image pairs show a significantly better performance of the HV (horizontal transmit, vertical receive) channel due to higher informativeness. On average, around four times as many vectors have been found using HV polarization. All software requirements necessary for applying the presented feature-tracking algorithm are open source to ensure a free and easy implementation.

DEVELOPMENT OF AN IMAGE-BASED FEATURE TRACKING ALGORITHM FOR REAL-TIME CLAD HEIGHT DETECTION

2006 ◽  
Vol 39 (16) ◽  
pp. 914-920 ◽  
Author(s):  
Mehrdad Iravani-Tabrizipour ◽  
Matthew Asselin ◽  
Ehsan Toyserkani
Keyword(s):  
Real Time ◽  
Feature Tracking ◽  
Tracking Algorithm

scholarly journals Feature Tracking for Target Identification in Acoustic Image Sequences

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jue Gao ◽  
Ya Gu ◽  
Peiyi Zhu
Keyword(s):  
Feature Tracking ◽  
Statistical Data ◽  
Target Identification ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
False Alarms ◽  
Quantitative Investigation ◽  
Acoustic Image ◽  
Feature Detectors ◽  
Underwater Target

This paper proposes underwater target identification with local features and a feature tracking algorithm for acoustic image sequences. Feature detectors and descriptors are key to feature tracking. Their performance in underwater scene is evaluated by the change of multitarget parameters. A comprehensive quantitative investigation into the performance of feature tracking is thereby presented. Experimental results confirm that the proposed algorithm can accurately track potential targets and determine whether the potential targets are static targets, dynamic targets, or false alarms according to the tracking trajectories and statistical data.

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