کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6952907 1451799 2008 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
HOG-assisted deep feature learning for pedestrian gender recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
HOG-assisted deep feature learning for pedestrian gender recognition
چکیده انگلیسی
Pedestrian gender recognition is a very challenging problem, since the viewpoint variations, illumination changes, occlusion, and poor quality are usually encountered in the pedestrian images. To address this problem, an effective HOG-assisted deep feature learning (HDFL) method is proposed in this paper. The key novelty lies in the design of HDFL network to effectively explore both deep-learned feature and weighted histogram of oriented gradient (HOG) feature for the pedestrian gender recognition. Specifically, the deep-learned and weighted HOG feature extraction branches are simultaneously performed on the input pedestrian image. A feature fusion process is subsequently conducted to obtain a more robust and discriminative feature, which is then fed to a softmax classifier for pedestrian gender recognition. Extensive experiments on multiple existing pedestrian image datasets have shown that the proposed HDFL method is able to effectively recognize the pedestrian gender, and consistently outperforms the state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Franklin Institute - Volume 355, Issue 4, March 2018, Pages 1991-2008
نویسندگان
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