Robust Pedestrian Detection Using Low Level and High Level Features
عنوان مقاله: Robust Pedestrian Detection Using Low Level and High Level Features
شناسه ملی مقاله: ICEE21_152
منتشر شده در بیست و یکمین کنفرانس مهندسی برق ایران در سال 1392
شناسه ملی مقاله: ICEE21_152
منتشر شده در بیست و یکمین کنفرانس مهندسی برق ایران در سال 1392
مشخصات نویسندگان مقاله:
Fariba Takarli - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Ali Aghagolzadeh
Hadi Seyedarabi
خلاصه مقاله:
Fariba Takarli - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Ali Aghagolzadeh
Hadi Seyedarabi
In this paper, we introduce pedestrian detection using combination of low level features like CNN, HOG and Haar with high level features. Two kinds of high level features were used in this paper. One is related to the probability of existence of human’s face, which obtained from combination of skin colorand possible location and area for the human’s face. The other is related to the probability of existence of human’s anti-bodywhich obtained by curvature checking of vertical edges, situation of them relative to each other and location of them in the detection window. Several different structures were studied and their results were compared on a diagram. Also the average execution times of them were gathered in a table. Atfirst, we show that appending the high level features to every low level feature improves the performance of detection very much and then, with proper arrangement of several features, it is possible to improve the performance of detection further without increasing the execution time. For evaluation of the proposed algorithm, INRIA database and a video sequence were used.
کلمات کلیدی: convolutional neural network, pedestrian detection, face detection, anti-body detection, HOG
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/208209/