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Algorithms 2018, 11(12), 192; https://doi.org/10.3390/a11120192

A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement

1
College of Electronic and Information Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China
2
Beijing Urban Construction Design & Development Group Co., Ltd., Beijing 100037, China
*
Author to whom correspondence should be addressed.
Received: 13 September 2018 / Revised: 10 November 2018 / Accepted: 21 November 2018 / Published: 26 November 2018
(This article belongs to the Special Issue Deep Learning for Image and Video Understanding)
Full-Text   |   PDF [11769 KB, uploaded 26 November 2018]   |  

Abstract

At present, the problem of pedestrian detection has attracted increasing attention in the field of computer vision. The faster regions with convolutional neural network features (Faster R-CNN) are regarded as one of the most important techniques for studying this problem. However, the detection capability of the model trained by faster R-CNN is susceptible to the diversity of pedestrians’ appearance and the light intensity in specific scenarios, such as in a subway, which can lead to the decline in recognition rate and the offset of target selection for pedestrians. In this paper, we propose the modified faster R-CNN method with automatic color enhancement (ACE), which can improve sample contrast by calculating the relative light and dark relationship to correct the final pixel value. In addition, a calibration method based on sample categories reduction is presented to accurately locate the target for detection. Then, we choose the faster R-CNN target detection framework on the experimental dataset. Finally, the effectiveness of this method is verified with the actual data sample collected from the subway passenger monitoring video. View Full-Text
Keywords: subway pedestrian detection; sample calibration; faster R-CNN; automatic color enhancement (ACE); false and miss detection subway pedestrian detection; sample calibration; faster R-CNN; automatic color enhancement (ACE); false and miss detection
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Qu, H.; Wang, M.; Zhang, C.; Wei, Y. A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement. Algorithms 2018, 11, 192.

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