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:2019,28(5):232-237
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结合对象分割的运动行人检测
(中国石油大学(华东) 计算机与通信工程学院, 青岛 266580)
Moving Pedestrian Detection Framework with Object Segmentation
(Department of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2018-11-21    修订日期:2018-12-12
中文摘要: 目标检测大量应用于监控系统的行人检测以及人脸识别,是当前深度学习的研究热点.监督学习利用人工标注大量数据集训练出针对特定场景的行人检测器.但是人工标注方法费时费力,本文针对监督学习需要人工标注数据集的缺点,研究了一种半自动标注行人的方法.针对静止的单目摄像机拍摄的监控视频,利用光流信息提供的初始前景可能性,以及跨越时间的视觉相似性来迭代地更新初始的前景可能性,分割出运动的行人,根据分割的前景对象,提出了一种半自动标注行人的方法.实验结果显示,本文的方法可以为行人检测系统提供大量数据集,且效率上明显优于传统人工标注的方法.
Abstract:Object detection is widely used in surveillance systems for pedestrian detection and face recognition. It is a research hotspot of current deep learning. Supervised learning trains pedestrian detectors for specific scenes by manually annotating large datasets. However, the manual labeling method is time-consuming and laborious. In this work, the shortcomings of manual labeling of datasets for supervised learning are studied. A method of semi-automatic labeling of pedestrians is proposed. The surveillance video captured by the stationary monocular camera, using the initial foreground possibilities provided by the optical flow information, and the visual similarity across time, iteratively updates the initial foreground likelihood to segment the moving pedestrians. According to the segmented foreground pedestrians, a method of semi-automatic labeling of pedestrians is proposed. The experimental results show that the proposed method can provide a large number of datasets for the pedestrian detection system, and the efficiency is obviously superior to the traditional manual annotation method.
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宫法明,吕轩轩,宫文娟,王晓宁.结合对象分割的运动行人检测.计算机系统应用,2019,28(5):232-237
GONG Fa-Ming,LYU Xuan-Xuan,GONG Wen-Juan,WANG Xiao-Ning.Moving Pedestrian Detection Framework with Object Segmentation.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):232-237

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