# A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement
## 1 Introduction
However, the histogram equalization is a method of adjusting the global image, which cannot effectively improve the local contrast [17; 18; 19]. In [20], researchers realize adaptive histogram equalization (AHE). Different from the histogram equalization, the AHE changes the image contrast by calculating the local histogram of the image, and then redistributes the luminance. Therefore, the algorithm is more suitable for improving the local contrast of the image to get more image details. However, AHE has the problem of over-amplifying the noise in the same area of the image. To solve this problem, the automatic color enhancement (ACE) algorithm is realized [21; 22; 23]. The algorithm calculates the luminance relationship between the target pixel and the surrounding pixels by difference methods, and the luminance is controlled by the luminance function, which is calculated by the spatial function and weighted processing. However, the complexity of the algorithm is higher. On this basis, [24] describes two fast approximations of ACE. First, use a polynomial approximation of the slope function to decompose the main computation into convolution. Second, an algorithm based on interpolating intensity levels also reduces the main computation to convolutions. Moreover, we present a review of the most recent works on pedestrian detection. Fast feature pyramids is an effective method for pedestrian detection, which proposes that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, the approximation yields considerable speedups with negligible loss in detection accuracy [25]. Ref [26] presents a novel method for detecting pedestrians under adverse illumination conditions, which model the relations between RGB and thermal data, then transfer to a deep network to output the detection results. This method is competitive with previous methods on the Caltech dataset. Ref [27] proposes a joint deep learning framework for pedestrian detection, which includes feature extraction, deformation handling, occlusion handling, and classification. This method can maximize the strength of each component through all the components interacting with each other. Ref [28] presents a solution on how to integrate the inaccurate scores of part detectors when there are occlusions on pedestrian detection. With the development of deep learning technology, there are more and more demands on the sample set. It is well known that the quality of samples directly influences the generalization effect of the model. Nowadays, there are many pedestrian detection databases, such as INRIA, ETH, and MIT. The INRIA and MIT database are mostly single targets which cannot meet the requirements of intensive passenger flow detection. The ETH database is very different from the actual subway scene in the light environment. In order to establish the experimental data, we collected the specific subway passenger monitoring videos and use the manual annotation method to make a large number of dense data samples. When we choose the faster R-CNN target detection framework to experiment on the dataset, the detection capability of the model trained by faster R-CNN is susceptible to the diversity of pedestrians' clothing and the light intensity, which can lead to the decline in recognition rate and the offset of bounding-box 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 bounding-box. In this paper, we reduce the sample category to two categories, filter the excess calibration target background, and improve the missing calibration box. Moreover, we test the performance under different category and different model.