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深度学习行人检测方法综述 被引量:20

An overview of deep learning based pedestrian detection algorithms
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摘要 行人检测技术在智能交通系统、智能安防监控和智能机器人等领域均表现出了极高的应用价值,已经成为计算机视觉领域的重要研究方向之一。得益于深度学习的飞速发展,基于深度卷积神经网络的通用目标检测模型不断拓展应用到行人检测领域,并取得了良好的性能。但是由于行人目标内在的特殊性和复杂性,特别是考虑到复杂场景下的行人遮挡和尺度变化等问题,基于深度学习的行人检测方法也面临着精度及效率的严峻挑战。本文针对上述问题,以基于深度学习的行人检测技术为研究对象,在充分调研文献的基础上,分别从基于锚点框、基于无锚点框以及通用技术改进(例如损失函数改进、非极大值抑制方法等)3个角度,对行人检测算法进行详细划分,并针对性地选取具有代表性的方法进行详细结合和对比分析。本文总结了当前行人检测领域的通用数据集,从数据构成角度分析各数据集应用场景。同时讨论了各类算法在不同数据集上的性能表现,对比分析各算法在不同数据集中的优劣。最后,对行人检测中待解决的问题与未来的研究方法做出预测和展望。如何缓解遮挡导致的特征缺失问题、如何应对单一视角下尺度变化问题、如何提高检测器效率以及如何有效利用多模态信息提高行人检测精度,均是值得进一步研究的方向。 Computer vision technology has been intensively developed nowadays and it is essential to facilitate image classification and human face identification.Machine learning based methods have been used as basic technologies to carry out computer vision tasks.The core of this technology is to distinguish the location and category of the target via manual image feature designation for targeted tasks.However,the manual design process is costly.Current emerging deep learning-based technology can automatically learn effective features from labeled or unlabeled data in a supervised or unsupervised manner and facilitate image recognition and target detection tasks.Deep learning based pedestrian detection technology is one of the aspects its development.Our pedestrian detection is to identify pedestrian targets in a scenario of input single frame image or image sequence and determine the localization of the pedestrians in the targeted image.Due to the complicated scenarios and the uniqueness of pedestrian targets,deep learning based pedestrian detection technology has challenged two key issues shown below:1)one aspect is the occlusion issue.The other one is that,the human body structure information of pedestrians is severely affected in the case of severe occlusion.As a result,the visual features of the occluded pedestrians are differentiated from those of the un-occluded ones leading to false negatives during inference.Due to the diversity of occlusion patterns,it is challenged to analyze which part is occluded accurately,and locates on-site capability for pedestrian detection algorithms;2)the other challenge is scale-based variance.The pedestrians’detection status is constrained of crowded or sparse scenariol.For a tiny target,due to the lack of sufficient semantic information,the detector is likely to misjudge it as background noise.Simultaneously,it is challenged for a set of clear anchors that can match it perfectly for a large-scale target during the training procedure.Moreover,large-scale pedestrian instances often
作者 罗艳 张重阳 田永鸿 郭捷 孙军 Luo Yan;Zhang Chongyang;Tian Yonghong;Guo Jie;Sun Jun(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China;School of Cyber Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处 《中国图象图形学报》 CSCD 北大核心 2022年第7期2094-2111,共18页 Journal of Image and Graphics
基金 国家重点研发计划资助(2017YFB1002400) 国家自然科学基金项目(61971281) 上海市重点实验室项目(18DZ2270700)。
关键词 行人检测 深度学习 卷积神经网络(CNN) 遮挡目标检测 小目标检测 pedestrian detection deep learning convolutional neural network(CNN) occlusion target detection smallscale target detection
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