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Object recognition using Haar features and histograms of oriented gradients

机译:使用Haar特征和定向梯度直方图进行目标识别

摘要

A system and method to detect objects in a digital image. At least one image representing at least one frame of a video sequence is received. A sliding window of different window sizes at different locations is placed in the image. A cascaded classifier including a plurality of increasingly accurate layers is applied to each window size and each location. Each layer includes a plurality of classifiers. An area of the image within a current sliding window is evaluated using one or more weak classifiers in the plurality of classifiers based on at least one of Haar features and Histograms of Oriented Gradients features. An output of each weak classifier is a weak decision as to whether the area of the image includes an instance of an object of a desired object type. A location of the zero or more images associated with the desired object type is identified.
机译:一种检测数字图像中的对象的系统和方法。接收表示视频序列的至少一帧的至少一个图像。在图像中放置了在不同位置具有不同窗口大小的滑动窗口。将包括多个精度越来越高的层的级联分类器应用于每个窗口大小和每个位置。每一层包括多个分类器。基于Haar特征和定向梯度直方图特征中的至少一个,使用多个分类器中的一个或多个弱分类器来评估当前滑动窗口内的图像区域。每个弱分类器的输出是关于图像的区域是否包括所需对象类型的对象的实例的弱判定。识别与期望的对象类型相关联的零个或更多个图像的位置。

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