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TED: A texture-edge descriptor for pedestrian detection in video sequences

机译:TED:用于视频序列中行人检测的纹理边缘描述符

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摘要

This paper presents a novel descriptor, TED, for pedestrian detection in video sequences. TED describes texture and edge information simultaneously. TED is a local descriptor because it is defined over a neighborhood. The size of the TED, independent of the neighborhood size defined over it, is 8 bits. TED is based on intensity difference, and so it is robust against illumination changes. We demonstrate TED performance in a block-based framework for pedestrian detection. Experimental results show the effectiveness of the proposed descriptor when applied in different outdoor and indoor environments.
机译:本文提出了一种新颖的描述符TED,用于视频序列中的行人检测。 TED同时描述纹理和边缘信息。 TED是本地描述符,因为它是在邻居上定义的。 TED的大小与8位无关,与附近定义的邻居大小无关。 TED基于强度差异,因此对于光照变化具有鲁棒性。我们在行人检测的基于块的框架中演示了TED的性能。实验结果表明,所提出的描述符在不同的室内和室外环境中均有效。

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