AUTHORS: Francisco Sanchez-Fernandez, Metzli Ramirez-Martinez, Philippe Brunet, Sidi-Mohammed Senouci, El-Bay Bourennane

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ABSTRACT: Multi-resolution object detection faces several drawbacks including its high dimensionality produced by a richer image representation in different channels or scales. In this paper, we propose a robust and lightweight multi-resolution method for vehicle detection using local binary patterns (LBP) as channel feature. Algorithm acceleration is done using LBP histograms instead of multi-scale feature maps and by extrapolating nearby scales to avoid computing each scale. We produce a feature descriptor capable of reaching a similar precision to other computationally more complex algorithms but reducing its size from 10 to 800 times. Finally, experiments show that our method can obtain accurate and considerably faster performance than state-of-the-art methods on vehicles datasets.

KEYWORDS: Feature extraction, texture, vehicle detection, Local Binary Patterns, features pyramids

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