Empirical Comparision on Boosted Cascade of Haar-like Features to Histogram of Oriented Gradients for Person Detection

Abdullah, Azizi; Albashish, Dheeb

doi:10.1109/iceei52609.2021.9611112

Cited by 1 publication

(1 citation statement)

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“…Therefore, the feature-extraction stage needs to consider segmentation inaccuracy for the effect of ship-detection performance. Haar-like is a feature description operator with excellent edge feature-extraction performance, which can be roughly divided into four types, namely edge feature, linear feature, center-surrounding feature, and diagonal feature [61]. Consequently, the proposed method exploits edge feature templates of Haar-like to achieve the boundary feature extraction as shown in Figure 4.…”

Section: Sp Segmentationmentioning

confidence: 99%

Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images

Pan

Yang

et al. 2023

Remote Sensing

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Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.

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Section: Sp Segmentationmentioning

confidence: 99%