基于角HOG特征的室外场景压缩对象表示方法

Tin-Tin Yu, Nu War
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引用次数: 3

摘要

目前,在物体跟踪、动作识别、自动视频监控等视觉应用系统中,从物体中提取HOG (Histogram of Gradient)特征,并将其用于分类任务。大多数HOG特征提取技术都是基于细胞和块的。尽管在当前的视觉系统中,细胞和块上的HOG特征是鲁棒的,但本文提出了一种以角点为中心的HOG特征提取方法。在多目标检测系统中,对单个或多个运动目标进行分类和标记,提取角点上的HOG特征。在室外挑战序列上进行HOG特征提取的对比实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Condensed object representation with corner HOG features for object classification in outdoor scenes
Nowadays, HOG (Histogram of Gradient) feature is extracted from the objects and using it in the classification tasks among the many visual application systems such as object tracking, action recognition and automated video surveillance. Most techniques of extraction HOG feature are based on cells and blocks. Although the HOG feature on cell and block are being robust for current visual systems, the alternative way to extract HOG feature that focus on corner points are presented in this paper. HOG features on corner points is extracted for multiple object detection system in which single or multiple moving objects are classified and labeled. And also comparison results on outdoor challenging sequences for HOG feature extraction on blocks and corners are provided with experimental results.
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