# A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement
## 1 Introduction  
As we know, the subway plays an important role in the urban public transport system, which can be exposed to a high security risk if the large quantities of passengers cannot be evacuated in a short time [1; 2; 3]. Thus, we need good means to analyze the high-density crowds. The current methods mainly include artificial statistics, infrared detection, video object detection, etc. The artificial statistics are unsustainable, and infrared detection has a low resolution. Therefore, more and more researchers pay attention to the video object detection, which can be combined with computer vision very well. In the field of computer vision, passenger flow detection has been studied for several decades, and it has been greatly improved regarding the detection accuracy with high efficiency. The researchers put forward a variety of algorithms. Document [4] adopts a local binary patterns (LBP) texture feature extraction method combined with a partial least squares regression (PLSR) model. However, this method is still in the experimental stage, and has not been widely applied. Document [5] collects the Haar features from pedestrian head-and-shoulder samples, and detects the pedestrian head using a strong classifier trained by AdaBoost algorithm. Furthermore, it further uses the bilinear interpolation algorithm to remove mistake detection windows with an inappropriate size. Document [6; 7; 8] makes comparisons between Haar-based and HOG-based algorithms on face recognition and pedestrian detection. The histogram of oriented gradients (HOG) extracts features by calculating the histogram of oriented gradients of the local region. Then, it uses the support vector machine (SVM) to train thisfeature set. The experimental results show that the passenger flow detection based on HOG features has a high success rate under the condition of integrated detected body with high image quality. However, for the case of partial body, the detection error can be very large. In [9; 10], researchers realize the object detection with deep learning. It uses selective search to get around 2000 region proposals from an image. Then, each region proposal is resized to a fixed size, and the convolution neural network (CNN) is used to extract features for the feature map. Finally, the feature vector is put into the SVM classifier to judge whether it belongs to a certain feature class, and the position of the region proposal is adjusted by the regression classifier. Compared to the HOG-based algorithm on pedestrian detection, R-CNN can extract more features, such as textures, materials, and light. However, one drawback is the large amount of overlap between the region proposals in the image, which can lead to repeat computation in the process of extracting features. In order to solve this problem, fast R-CNN proposes two improvements: first, the entire image is normalized and sent directly to CNN, and then the fifth convolution layer gets the features of each region proposal. The second step is to put the bounding-box regression into the neural network, with the region classifier, to a multi-task model. This method enables object detection to improve efficiency while ensuring accuracy [11]. However, finding all the region proposals is also a very time-consuming process. To solve this problem, the faster R-CNN uses the region proposal network to directly train and get region proposals, which promotes the efficiency compared to the fast R-CNN [12; 13; 14; 15; 16]. However, when the image quality is poor, or the background is close to the color of person, the accuracy of the detection has dropped sharply. Aiming at this problem, this paper proposes to use an image enhancement algorithm to deal with the images first. The histogram equalization is a commonly used method of image enhancement, whose basic idea is to determine a mapping curve by using the histogram of the gray distribution of the image and, finally, achieve the purpose of improving image contrast.