公开(公告)号：US20160379062A1

公开(公告)日：2016-12-29

申请号：US14519927

申请日：2014-10-21

Applicant: SRI International

Inventor： Saad Masood Khan ,  Hui Cheng ,  Dennis Lee Matthies ,  Harpreet Singh Sawhney ,  Sang-Hack Jung ,  Chris Broaddus ,  Bogdan Calin Mihai Matei ,  Ajay Divakaran

IPC: G06K9/00 ,  G06T7/20 ,  B64D47/08 ,  G06K9/46 ,  B64C39/02 ,  G06T7/00 ,  G06K9/62

CPC classification number: G06K9/00785 ,  B64C39/024 ,  B64C2201/123 ,  B64D47/08 ,  G06K9/00651 ,  G06K9/4671 ,  G06K9/6269 ,  G06K9/6282 ,  G06T7/11 ,  G06T7/20 ,  G06T2207/10012

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract translation: 公开了一种用于确定在图像中检测到的车辆的车辆类型的计算机实现的方法。 接收具有检测到的车辆的图像。 在检测到的车辆上投影出具有显着特征点的多个车辆模型。 将来自车辆模型的每个突出特征位置的第一组特征与从所检测到的车辆的相应突出特征位置导出的第二组特征进行比较，以形成一组正的匹配得分（p分数）和 一组负面比赛得分（n分）。 检测到的车辆至少部分地基于p分数集合和n分数集合被分类为车辆模型模型之一。

公开(公告)号：US09977972B2

公开(公告)日：2018-05-22

申请号：US14519927

申请日：2014-10-21

Applicant: SRI International

Inventor： Saad Masood Khan ,  Hui Cheng ,  Dennis Lee Matthies ,  Harpreet Singh Sawhney ,  Sang-Hack Jung ,  Chris Broaddus ,  Bogdan Calin Mihai Matei ,  Ajay Divakaran

IPC: G06K9/00 ,  G06K9/46 ,  B64C39/02 ,  B64D47/08 ,  G06K9/62 ,  G06T7/20 ,  G06T7/11

CPC classification number: G06K9/00785 ,  B64C39/024 ,  B64C2201/123 ,  B64D47/08 ,  G06K9/00651 ,  G06K9/4671 ,  G06K9/6269 ,  G06K9/6282 ,  G06T7/11 ,  G06T7/20 ,  G06T2207/10012

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models based at least in part on the set of p-scores and the set of n-scores.