Robust background subtraction for automated detection and tracking of targets in wide area motion imagery

2012 ◽  
Author(s):  
Phil Kent ◽  
Simon Maskell ◽  
Oliver Payne ◽  
Sean Richardson ◽  
Larry Scarff
Keyword(s):  
Background Subtraction ◽  
Automated Detection ◽  
Wide Area ◽  
Detection And Tracking ◽  
Motion Imagery

scholarly journals 3D city scale reconstruction using wide area motion imagery

2018 ◽  
Author(s):  
◽  
Raphael Viguier
Keyword(s):  
3D Reconstruction ◽  
Data Compression ◽  
Data Augmentation ◽  
Point Clouds ◽  
Wide Area ◽  
Large Field ◽  
Reconstruction Algorithms ◽  
Detection And Tracking ◽  
3D Point Clouds ◽  
Motion Imagery

3D reconstruction is one of the most challenging but also most necessary part of computer vision. It is generally applied everywhere, from remote sensing to medical imaging and multimedia. Wide Area Motion Imagery is a field that has gained traction over the recent years. It consists in using an airborne large field of view sensor to cover a typically over a square kilometer area for each captured image. This is particularly valuable data for analysis but the amount of information is overwhelming for any human analyst. Algorithms to efficiently and automatically extract information are therefore needed and 3D reconstruction plays a critical part in it, along with detection and tracking. This dissertation work presents novel reconstruction algorithms to compute a 3D probabilistic space, a set of experiments to efficiently extract photo realistic 3D point clouds and a range of transformations for possible applications of the generated 3D data to filtering, data compression and mapping. The algorithms have been successfully tested on our own datasets provided by Transparent Sky and this thesis work also proposes methods to evaluate accuracy, completeness and photo-consistency. The generated data has been successfully used to improve detection and tracking performances, and allows data compression and extrapolation by generating synthetic images from new point of view, and data augmentation with the inferred occlusion areas.

Detection and tracking performance with compressed wide area motion imagery

2012 ◽  
Author(s):  
Patrick C. Hytla ◽  
Kevin S. Jackovitz ◽  
Eric J. Balster ◽  
Juan R. Vasquez ◽  
Michael L. Talbert
Keyword(s):  
Tracking Performance ◽  
Wide Area ◽  
Detection And Tracking ◽  
Motion Imagery

scholarly journals HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hakki Motorcu ◽  
Hasan F. Ates ◽  
H. Fatih Ugurdag ◽  
Bahadir K. Gunturk
Keyword(s):  
Wide Area ◽  
Detection And Tracking ◽  
Motion Imagery ◽  
Object Center

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

Automated Detection and Tracking of Adult Pacific Lampreys in Underwater Video Collected at Snake and Columbia River Fishways

2014 ◽  
Vol 34 (1) ◽  
pp. 111-118 ◽  
Author(s):  
Cristi Negrea ◽  
Donald E. Thompson ◽  
Steven D. Juhnke ◽  
Derek S. Fryer ◽  
Frank J. Loge
Keyword(s):  
Columbia River ◽  
Automated Detection ◽  
Underwater Video ◽  
Detection And Tracking

Learning with Privileged Information for Improved Target Classification

2014 ◽  
Vol 2 (3) ◽  
pp. 50-66 ◽  
Author(s):  
Roman Ilin ◽  
Simon Streltsov ◽  
Rauf Izmailov
Keyword(s):  
Classification Accuracy ◽  
Wide Area ◽  
Future Research ◽  
Target Classification ◽  
Privileged Information ◽  
Training Time ◽  
Additional Information ◽  
Motion Imagery ◽  
Learning Using Privileged Information

This work considers “Learning Using Privileged Information” (LUPI) paradigm. LUPI improves classification accuracy by incorporating additional information available at training time and not available during testing. In this contribution, the LUPI paradigm is tested on a Wide Area Motion Imagery (WAMI) dataset and on images from the Caltech 101 dataset. In both cases a consistent improvement in classification accuracy is observed. The results are discussed and the directions of future research are outlined.

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