Light detection and ranging system
    12.
    发明授权

    公开(公告)号:US10884108B2

    公开(公告)日:2021-01-05

    申请号:US15823750

    申请日:2017-11-28

    Abstract: A light detection and ranging system comprises an optical transmitter for beam scanning on a scan region, and receiving reflected light from the scan region; and an optical receiver for directing the reflected light for signal conversion. The optical transmitter includes a beam refractive unit including optical refractive devices, rotatably disposed about a rotation axis, for directing a collimated laser beam from a first optical path towards a direction depending on rotation angles of the optical refractive devices; and a motion unit for actuating relative motion of the optical refractive devices so as to perform beam scanning towards directions on the scan region. The optical receiver includes an off-axis reflective unit, disposed in the first optical path, for directing the reflected light towards a second optical path; and a light detection unit, disposed in the second optical path, for performing signal conversion on the reflected light.

    All-weather thermal-image pedestrian detection method

    公开(公告)号:US10198657B2

    公开(公告)日:2019-02-05

    申请号:US15375438

    申请日:2016-12-12

    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.

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