<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>HOG Feature is the mainstream feature applied in the field of pedestrian detection .HOG combined with CSS has good effects on pedestrian detection. Because of the large amount calculation of HOG and CSS, HOG and CSS has poor real-time performance, we propose LCSSF (Local Color Self Similarity Feature) avoiding calculating the global color similarity distribution of CSS. The tested results of the Inria and the street pedestrian database show that the accuracy of the HOG with LCSSF has better detection performance and better real-time performance than HOG and CSS.</p></abstract>

HOG Feature is the mainstream feature applied in the field of pedestrian detection .HOG combined with CSS has good effects on pedestrian detection. Because of the large amount calculation of HOG and CSS, HOG and CSS has poor real-time performance, we propose LCSSF (Local Color Self Similarity Feature) avoiding calculating the global color similarity distribution of CSS. The tested results of the Inria and the street pedestrian database show that the accuracy of the HOG with LCSSF has better detection performance and better real-time performance than HOG and CSS.

Pedestrian Detection Algorithm Based on Local Color Parallel Similarity Features

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Pedestrian Detection Algorithm Based on Local Color Parallel Similarity Features"}

HOG Feature is the mainstream feature applied in the field of pedestrian detection .HOG combined with CSS has good effects on pedestrian detection. Because of...

Pedestrian Detection Algorithm Based on Local Color Parallel Similarity Features

Published Online: Dec 16, 2013

Page range: 1869 - 1890

Received: Jul 01, 2013

Accepted: Oct 30, 2013

DOI: https://doi.org/10.21307/ijssis-2017-618

© 2013 Xianxian Tian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.