首页> 外文会议>Australian Joint Conference on Artificial Intelligence; 20071202-06; Gold Coast(AU) >A Comparison of Neural-Based Techniques Investigating Rotational Invariance for Upright People Detection in Low Resolution Imagery
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A Comparison of Neural-Based Techniques Investigating Rotational Invariance for Upright People Detection in Low Resolution Imagery

机译:低分辨率图像中直立人检测的基于旋转不变性的基于神经的技术比较

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This paper describes a neural-based technique for detecting upright persons in low-resolution beach imagery in order to predict trends of tourist activities at beach sites. The proposed system uses a structural feature extraction technique to represent objects of interest for training a selection of neural classifiers. A number of neural-based classifiers are compared in this study and a direction-based feature extraction technique is investigated in conjunction with a rotationally invariant procedure for the purpose of beach object classification. Encouraging results are presented for person detection using video imagery collected from a beach site on the coast of Australia.
机译:本文介绍了一种基于神经的技术,用于在低分辨率海滩图像中检测直立的人,以预测海滩景点的游客活动趋势。所提出的系统使用结构特征提取技术来表示感兴趣的对象,以训练神经分类器的选择。在这项研究中对许多基于神经的分类器进行了比较,并针对海滩物体分类的目的,结合旋转不变过程研究了基于方向的特征提取技术。使用从澳大利亚海岸的海滩站点收集的视频图像,提供了令人鼓舞的结果以供人检测。

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