%0 Journal Article
%T A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement
%A Changnian Zhang
%A Hongquan Qu
%A Meihan Wang
%A Yun Wei
%J Algorithms | An Open Access Journal from MDPI
%D 2018
%R https://doi.org/10.3390/a11120192
%X 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-Tex
%K subway pedestrian detection
%K sample calibration
%K faster R-CNN
%K automatic color enhancement (ACE)
%K false and miss detection
%U https://www.mdpi.com/1999-4893/11/12/192