Object detection using voting spaces trained by few samples

Pei Xu; Mao Ye; Xue Li; Lishen Pei; Pengwei Jiao

doi:10.1117/1.OE.52.9.093105

16 September 2013 Object detection using voting spaces trained by few samples

Pei Xu, Mao Ye, Xue Li, Lishen Pei, Pengwei Jiao

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Optical Engineering, Vol. 52, Issue 9, 093105 (September 2013). https://doi.org/10.1117/1.OE.52.9.093105

Abstract

A method to detect generic objects by training with a few image samples is proposed. A new feature, namely locally adaptive steering (LAS), is proposed to represent local principal gradient orientation information. A voting space is then constructed in terms of cells that represent query image coordinates and ranges of feature values at corresponding pixel positions. Cell sizes are trained in voting spaces to estimate the tolerance of object appearance at each pixel location. After that, two detection steps are adopted to locate instances of object class in a given target image. At the first step, patches of objects are recognized by densely voting in voting spaces. Then, the refined hypotheses step is carried out to accurately locate multiple instances of object class. The new approach is training the voting spaces based on a few samples of the object. Our approach is more efficient than traditional template matching approaches. Compared with the state-of-the-art approaches, our experiments confirm that the proposed method has a better performance in both efficiency and effectiveness.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Pei Xu, Mao Ye, Xue Li, Lishen Pei, and Pengwei Jiao "Object detection using voting spaces trained by few samples," Optical Engineering 52(9), 093105 (16 September 2013). https://doi.org/10.1117/1.OE.52.9.093105

Published: 16 September 2013

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