公开(公告)号：US20180365511A1

公开(公告)日：2018-12-20

申请号：US15628111

申请日：2017-06-20

Applicant: NATIONAL CHUNG SHAN INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： CHUN-WEI HSIEH ,  SHIH-CHE CHIEN ,  FENG-CHIA CHANG ,  CHIEN-HAO HSIAO

IPC: G06K9/32 ,  G06K9/62

CPC classification number: G06K9/3241 ,  G06K9/6231

Abstract: A method of speeding up image detection, adapted to increase a speed of detecting a target image and enhance efficiency of image detection, comprises the steps of capturing an image; retrieving a plurality of characteristic points of the image; creating a region of interest (ROI) centered at the characteristic points each; creating a plurality of search point scan windows corresponding to the ROIs, respectively; calculating target hit scores of the characteristic points and the search point scan windows; comparing the target hit scores of the characteristic points and the search point scan windows to obtain an ROI most likely to have a target image; calculating centroid coordinates of the ROI by a centroid shift weight equation; and narrowing a scope of ROI search according to a location of the centroid coordinates and reducing a displacement between the search points.

公开(公告)号：US20180165552A1

公开(公告)日：2018-06-14

申请号：US15375438

申请日：2016-12-12

Applicant: NATIONAL CHUNG SHAN INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： SHIH-SHINH HUANG ,  SHIH-CHE CHIEN ,  FENG-CHIA CHANG ,  CHIEN-HAO HSIAO ,  YU-SUNG HSIAO

IPC: G06K9/62 ,  H04N19/176 ,  G06K9/46

CPC classification number: G06K9/4642 ,  G06K9/00369 ,  G06K9/00791 ,  G06K9/6269 ,  H04N19/103 ,  H04N19/136 ,  H04N19/176 ,  H04N19/90

Abstract: An all-weather thermal-image pedestrian detection method includes (a) capturing diurnal thermal images and nocturnal thermal images of a same pedestrian and non-pedestrian object in a same defined block to create a sample database of thermal images, wherein the sample database comprises pedestrian samples and non-pedestrian samples; (b) performing LBP encoding on the pedestrian samples and the non-pedestrian samples, wherein complementary LBP codes in the same defined block are treated as identical LBP codes; (c) expressing the LBP codes in the same defined block as features by a gradient direction histogram (HOG) to obtain feature training samples of the pedestrian samples and the non-pedestrian samples; (d) entering the feature training samples into a SVM to undergo training by Adaboost so as to form a strong classifier; and (e) effectuating pedestrian detection by searching the strong classifiers in thermal images with sliding window technique to detect for presence of pedestrians.