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Proceedings Paper

Air-ground vehicle detection with a reduced object category specific visual dictionary
Author(s): Lizuo Jin; Yanchao Dong; Qinghan Xu; Feiran Jie
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Paper Abstract

Nowadays ground vehicle detection on airborne platforms is becoming very important for intelligent visual surveillance applications. Object detection using cascade structured classifiers is booming fast in recent decade, and very successful in real-time applications. However, most of them apply a sliding window on multi-scaled images which commonly need heavy computational expense, therefore, are only suitable for using simple features. In this paper, a biologically inspired object detection algorithm is proposed, which exploits image patch based feature learning and visual saliency detection. The image patch based local features are learnt by unsupervised learning to generate an object category specific visual dictionary. Visual saliency detection is performed to extract candidate object regions from a whole image using the learnt local features. Instead of a sliding window, a candidate object region is sent to an object classifier only when its features are salient on the whole image. Since the number of candidate object regions decreases dramatically, it allows to utilize much complex features to represent object images so that it can increase the descriptive capability of the learnt features. The experimental results on practical vehicle image datasets indicate that less computational expense and good detection performance can be achieved.

Paper Details

Date Published: 26 October 2013
PDF: 8 pages
Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 89180G (26 October 2013); doi: 10.1117/12.2031531
Show Author Affiliations
Lizuo Jin, Southeast Univ. (China)
Science and Technology on Electro-Optic Control Lab. (China)
Yanchao Dong, Southeast Univ. (China)
Qinghan Xu, Southeast Univ. (China)
Feiran Jie, Science and Technology on Electro-Optic Control Lab. (China)


Published in SPIE Proceedings Vol. 8918:
MIPPR 2013: Automatic Target Recognition and Navigation
Tianxu Zhang; Nong Sang, Editor(s)

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