Abstract
This paper presents a novel formulation for detecting objects with articulated rigid bodies from high-resolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects object patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain object parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain objects. In this stage, we increase the size of the image patches so that they include the complete object using models of the object parts. Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive objects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.
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Project supported by the China Knowledge Centre for Engineering Sciences and Technology (No. CKCEST-2014-1-2), the Zhejiang Provincial Natural Science Foundation of China (No. LY14F020027), and the National Natural Science Foundation of China (No. 61272304)
ORCID: Xun LIU, http://orcid.org/0000-0002-3045-2943
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Liu, X., Zhang, Y., Zhang, Sy. et al. Detection of engineering vehicles in high-resolution monitoring images. Frontiers Inf Technol Electronic Eng 16, 346–357 (2015). https://doi.org/10.1631/FITEE.1500026
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DOI: https://doi.org/10.1631/FITEE.1500026