Imperial College London
Dr Panagiota (Tania) Stathaki

Dr Panagiota (Tania) Stathaki

Faculty of Engineering, Department of Electrical and Electronic Engineering

Reader in Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6229t.stathaki Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

812Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{ElMikaty:2017:10.23919/MVA.2017.7986801,
author = {ElMikaty, M and Stathaki, P},
doi = {10.23919/MVA.2017.7986801},
publisher = {IEEE},
title = {Detection of cars in complex urban areas},
url = {http://dx.doi.org/10.23919/MVA.2017.7986801},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Detection of cars in airborne images of typical urbanareas has various applications in several domains,such as surveillance, military and remote sensing. Itis a tremendously-challenging problem, mainly becauseof the significant inter-class similarity among variousobjects in urban environments. In this paper, a novelframework is introduced that adopts a sliding-windowapproach and it depicts, in a novel way, the local distributionof gradients, colours and texture. A linear supportvector machine classifier is used to differentiatebetween descriptors that belong to cars and descriptorsthat belong to other objects in a hyperspace of 3838dimensions. Descriptors are computed over a newlyproposedadaptive distribution of cells that enables theuse of various rotation-variant image descriptors. Theproposed framework has been evaluated on the Vaihingendataset and results corroborate its superiority as itachieves a higher precision for a given recall than thestate of the art.
AU  - ElMikaty,M
AU  - Stathaki,P
DO  - 10.23919/MVA.2017.7986801
PB  - IEEE
PY  - 2017///
TI  - Detection of cars in complex urban areas
UR  - http://dx.doi.org/10.23919/MVA.2017.7986801
UR  - http://hdl.handle.net/10044/1/49427
ER  -
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