<abstract xmlns="http://www.w3.org/1999/xhtml"><p> With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG) features of the human body will show great different between front &amp; back standing (F&amp;B) and side standing (Side) human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&amp;B human body and not F&amp;B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.</p></abstract>

With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG) features of the human body will show great different between front & back standing (F&B) and side standing (Side) human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.

Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

Container Supply Chain Tech. Engineering Research Center, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

Logistics Engineering College, Shanghai Maritime University, No.1550 Haigang Ave, Shanghai 201306, China

Polish Maritime Research

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

With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation...

Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

Published Online: Oct 15, 2015

Page range: 163 - 170

DOI: https://doi.org/10.1515/pomr-2015-0049

Keywords
Human Detection, Histograms of Oriented Gradients, Support Vector Machine, Classification

© 2015

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

Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

Published Online: Oct 15, 2015

Page range: 163 - 170

DOI: https://doi.org/10.1515/pomr-2015-0049

KeywordsHuman Detection, Histograms of Oriented Gradients, Support Vector Machine, Classification

© 2015

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

Keywords
Human Detection, Histograms of Oriented Gradients, Support Vector Machine, Classification