Background Filtering for Improving of Object Detection in Images

We propose a method for improving object recognition in street scene images by identifying and filtering out background aspects. We analyse the semantic relationships between foreground and background objects and use the information obtained to remove areas of the image that are misclassified as for...

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Bibliographic Details
Published in2010 20th International Conference on Pattern Recognition pp. 922 - 925
Main Authors Ge Qin, Vrusias, Bogdan, Gillam, Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
background detection
Filtering
Image edge detection
Object recognition
scene understanding
semantic modelling
Semantics
Shape
Vehicles
Visualization
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Summary:We propose a method for improving object recognition in street scene images by identifying and filtering out background aspects. We analyse the semantic relationships between foreground and background objects and use the information obtained to remove areas of the image that are misclassified as foreground objects. We show that such background filtering improves the performance of four traditional object recognition methods by over 40%. Our method is independent of the recognition algorithms used for individual objects, and can be extended to generic object recognition in other environments by adapting other object models.
ISBN:1424475422
9781424475421
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.231