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    TRAFFIC CONDITION PREDICTION SYSTEM AND TRAFFIC CONDITION PREDICTION METHOD
    2.
    发明申请
    TRAFFIC CONDITION PREDICTION SYSTEM AND TRAFFIC CONDITION PREDICTION METHOD 有权

    公开(公告)号：US20220036727A1

    公开(公告)日：2022-02-03

    申请号：US17019249

    申请日：2020-09-12

    Applicant: Institute For Information Industry

    Inventor： Yen-Lin CHEN ,  Hsiu-Chih CHEN ,  Chao-Wei YU ,  Chieh-Min CHANG ,  Meng-Tsan LI

    IPC: G08G1/01 ,  G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06T7/73

    Abstract: A traffic condition prediction system and a traffic condition prediction method are disclosed. The method includes: determining a center of circle in a surveillance image of a target traffic scene; determining a first circle based on the center of circle and a first radius; extracting a plurality of first feature points along the circumference of the first circle according to a first preset sampling frequency; generating a scene feature of the target traffic scene at least based on the first feature points; determining whether the scene feature and a scene feature of another traffic scene are similar; and when determining that they are similar, predicting traffic condition of the target traffic scene through a prediction model used for predicting traffic condition of the other traffic scene. The scene feature of the target traffic scene and that of the other traffic scene are generated in a same way.

    MULTI-CLASS OBJECT CLASSIFYING METHOD AND SYSTEM
    3.
    发明申请
    MULTI-CLASS OBJECT CLASSIFYING METHOD AND SYSTEM 有权
    多类对象分类方法与系统

    公开(公告)号：US20160162757A1

    公开(公告)日：2016-06-09

    申请号：US14562787

    申请日：2014-12-08

    Applicant: INSTITUTE FOR INFORMATION INDUSTRY

    Inventor： Yen-Lin CHEN ,  Chuan-Yen CHIANG ,  Chao-Wei YU ,  Augustine TSAI ,  Meng-Tsan LI

    IPC: G06K9/62 ,  G06T3/20 ,  G06T5/40 ,  G06K9/46 ,  G06T5/00

    CPC classification number: G06T3/20 ,  G06K9/628 ,  G06K9/6286

    Abstract: A multi-class object classifying method and system are disclosed herein, where the multi-class object classifying method includes the following steps: classes, first training images and second training images are received and stored, and first characteristic images and second characteristic images are respectively extracted from the first training images and the second training images; the first training images is used to generate classifiers through a linear mapping classifying method; a classifier and the second characteristic images are used to determine parameter ranges corresponding to the classes and a threshold corresponding to the classifier. When two of the parameter ranges overlap, the remaining parameter ranges except for the two overlapped parameter ranges are recorded; after another classifier is selected from the classifiers except for the classifier that has been selected, the previous steps is repeated until the parameter ranges don't overlap with each other and the parameter ranges are recorded.

    Abstract translation: 本文公开了一种多类对象分类方法和系统，其中多类对象分类方法包括以下步骤：接收和存储类，第一训练图像和第二训练图像，并且第一特征图像和第二特征图像分别 从第一训练图像和第二训练图像中提取; 第一训练图像用于通过线性映射分类方法生成分类器; 分类器和第二特征图像用于确定与类别相对应的参数范围和对应于分类器的阈值。 当两个参数范围重叠时，除了两个重叠的参数范围之外的其余参数范围被记录; 在除了已经选择的分类器之外的分类器中选择另一个分类器之后，重复前面的步骤，直到参数范围彼此不重叠并且记录参数范围。

    FEATURE DETERMINATION APPARATUS AND METHOD ADAPTED TO MULTIPLE OBJECT SIZES
    7.
    发明申请
    FEATURE DETERMINATION APPARATUS AND METHOD ADAPTED TO MULTIPLE OBJECT SIZES 审中-公开

    公开(公告)号：US20200151492A1

    公开(公告)日：2020-05-14

    申请号：US16391621

    申请日：2019-04-23

    Applicant: Institute For Information Industry

    Inventor： Yen-Lin CHEN ,  Hong-Yi LIANG ,  Xiu-Zhi CHEN ,  Chao-Wei YU ,  Meng-Tsan LI

    IPC: G06K9/46 ,  G06N3/04 ,  G06K9/62

    Abstract: A feature determination apparatus and method adapted to multiple object sizes are provided. The apparatus individually supplies each of the object images to a convolution neural network having several convolution layers to generate multiple feature maps corresponding to each object image. The apparatus calculates a feature amount of each feature image of each object image. The apparatus determines an invalid layer start number of each object image according to a preset threshold and the feature amount corresponding to each object image. The apparatus determines a feature map extraction recommendation for each of a plurality of object sizes according to a size of each object image and the invalid layer start number of each object image.

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