A joint approach to vector road map registration and vehicle tracking for wide area motion imagery

2016 ◽  
Author(s):  
Ahmed Elliethy ◽  
Gaurav Sharma
Keyword(s):  
Vehicle Tracking ◽  
Wide Area ◽  
Road Map ◽  
Motion Imagery ◽  
Joint Approach ◽  
Map Registration

Moving object detection for vehicle tracking in Wide Area Motion Imagery using 4D filtering

2016 ◽  
Author(s):  
Kannappan Palaniappan ◽  
Mahdieh Poostchi ◽  
Hadi Aliakbarpour ◽  
Raphael Viguier ◽  
Joshua Fraser ◽  
...  
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Vehicle Tracking ◽  
Wide Area ◽  
Motion Imagery

Vehicle-tracking in wide-area motion imagery from an airborne platform

SPIE Newsroom ◽  
2015 ◽  
Author(s):  
Jasper van Huis ◽  
Adam van Eekeren ◽  
Jan Baan ◽  
Pieter Eendebak
Keyword(s):  
Vehicle Tracking ◽  
Wide Area ◽  
Motion Imagery

Vehicle tracking in wide area motion imagery from an airborne platform

2015 ◽  
Author(s):  
Adam W. M. van Eekeren ◽  
Jasper R. van Huis ◽  
Pieter T. Eendebak ◽  
Jan Baan
Keyword(s):  
Vehicle Tracking ◽  
Wide Area ◽  
Motion Imagery

Road-Map Aided GM-PHD Filter for Multi-Vehicle Tracking with Automotive Radar

2021 ◽  
pp. 1-1
Author(s):  
Kun Shi ◽  
Zhiguo Shi ◽  
Chaoqun Yang ◽  
Shibo He ◽  
Jiming Chen ◽  
...  
Keyword(s):  
Vehicle Tracking ◽  
Automotive Radar ◽  
Road Map

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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