公开(公告)号：US20150235105A1

公开(公告)日：2015-08-20

申请号：US14469635

申请日：2014-08-27

Applicant: Electronics and Telecommunications Research Institute

Inventor： Byung-Gil HAN ,  Kil-Taek LIM ,  Yun-Su CHUNG ,  Soo-In LEE

IPC: G06K9/62 ,  G06N99/00

CPC classification number: G06K9/6857

Abstract: An apparatus for rapidly detecting an object of interest includes: a first object of interest detector configured to determine a region of an object of interest for an image, from which the object of interest is to be detected, by using a first training image; and a second object of interest detector configured to detect the object of interest from the region of the object of interest determined by the first object of interest detector by using a second training image, which is bigger in size than the first training image.

Abstract translation: 一种用于快速检测感兴趣对象的装置，包括：第一感兴趣对象检测器，其被配置为通过使用第一训练图像来确定要检测感兴趣对象的图像的感兴趣对象的区域; 以及第二感兴趣对象检测器，被配置为通过使用尺寸比第一训练图像大的第二训练图像，从感兴趣对象检测器确定的感兴趣对象区域中检测感兴趣对象。

公开(公告)号：US20140177946A1

公开(公告)日：2014-06-26

申请号：US13959310

申请日：2013-08-05

Applicant: Electronics and Telecommunications Research Institute

Inventor： Kil-Taek LIM ,  Yun-Su CHUNG ,  Byung-Gil HAN ,  Eun-Chang CHOI ,  Soo-In LEE

IPC: G06K9/00

CPC classification number: G06K9/00369 ,  G06K9/4614 ,  G06K9/4642

Abstract: Disclosed herein is an apparatus and method for detecting a person from an input video image with high reliability by using gradient-based feature vectors and a neural network. The human detection apparatus includes an image preprocessing unit for modeling a background image from an input image. A moving object area setting unit sets a moving object area in which motion is present by obtaining a difference between the input image and the background image. A human region detection unit extracts gradient-based feature vectors for a whole body and an upper body from the moving object area, and detects a human region in which a person is present by using the gradient-based feature vectors for the whole body and the upper body as input of a neural network classifier. A decision unit decides whether an object in the detected human region is a person or a non-person.

Abstract translation: 本发明公开了一种通过使用基于梯度的特征向量和神经网络从具有高可靠性的输入视频图像中检测人的装置和方法。 人体检测装置包括用于从输入图像建模背景图像的图像预处理单元。 移动物体区域设定单元通过获得输入图像与背景图像之间的差异来设定存在运动的移动物体区域。 人区域检测单元从运动对象区域提取针对全身和上身的基于梯度的特征向量，并且通过使用用于全身的基于梯度的特征向量来检测存在人的人区域， 上身作为神经网络分类器的输入。 决定单元判定检测到的人类区域中的对象是人还是非人。

公开(公告)号：US20140169664A1

公开(公告)日：2014-06-19

申请号：US13959288

申请日：2013-08-05

Applicant: Electronics and Telecommunications Research Institute

Inventor： Byung-Gil HAN ,  Yun-Su CHUNG ,  Kil-Taek LIM ,  Eun-Chang CHOI ,  Soo-In LEE

IPC: G06K9/00

CPC classification number: G06K9/00362 ,  G06K9/6256 ,  G06K9/6269 ,  G06K2009/4666

Abstract: Disclosed herein are an apparatus and method for recognizing a human in an image. The apparatus includes a learning unit and a human recognition unit. The learning unit calculates a boundary value between a human and a non-human based on feature candidates extracted from a learning image, detects a feature candidate for which an error is minimized as the learning image is divided into the human and the non-human using the calculated boundary value, and determines the detected feature candidate to be a feature. The human recognition unit extracts a candidate image where a human may be present from an acquired image, and determines whether the candidate image corresponds to a human based on the feature that is determined by the learning unit.

Abstract translation: 这里公开了一种用于识别图像中的人的装置和方法。 该装置包括学习单元和人类识别单元。 学习单元基于从学习图像提取的特征候选来计算人与非人物之间的边界值，当将学习图像划分为人类和非人类使用时，检测错误最小化的特征候选 计算的边界值，并将所检测的特征候选确定为特征。 人类识别单元从获取的图像中提取可能存在人的候选图像，并且基于由学习单元确定的特征来确定候选图像是否对应于人。