Image Feature Selection Mechanism for Intruder Detection in Vision-based Sense and Avoid System
ZHONG Peiyi
CAO Yunfeng
DING Meng
Abstract：In this paper,an image feature selection mechanism under the framework of sparse representation is proposed for intruder detection in vision based on sense and avoid system. Based on the framework of Spatial Pyramid Matching using Sparse Cod?ing,the low-level feature usually adopts the Histogram of Oriented Gradient(HOG)feature descriptor or the Scale-Invariant Fea?ture Transform(SIFT)feature descriptor. In this paper,recall of the intruder detection results is used to compare the HOG feature descriptor and the SIFT feature descriptor under different weathers with complex background. The best-performing feature descrip?tor is chosen as the low-level feature of sc-SPM method. The experimental results show that the SIFT feature descriptor is more suit?able for a variety of different weathers and has better robustness.
Keywords：sense and avoiddetectionsc-SPMfeature extractionHOGSIFTedge-boxes
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 334-338,464 )