## 1 Introduction
Additionally, studies such as [3; 4] have applied the recurrent NN recognition method based on time sequences. In particular, [3] adopted long short-term Memory (LSTM), whereas [4] converted the convolution kernel in the standard CNN framework to a three-dimensional (3D) structure. However, compared with activity recognition, differences observed during gait recognition are extremely subtle. Consequently, gait recognition entails discarding intuitively or visually identifiable appearance cues to prevent changes in appearance from affecting the recognition results. Studies regarding other gait recognition methods involving NNs included the following. In [5], the CNN was applied to estimate posture in images to obtain the location of each body part, in order to input the observed sequence into LSTM for distinction. In [6], sensors were applied on human bodies to track the up-down oscillation of five body parts including the waist and hand; subsequently, the collected data were compiled to be inputted into the NN. In [7], the silhouette-based method was employed; this method captures joints such as the pelvis, waist, neck, and knees from the partitions of a silhouette and trains the NN using data on the relative locations of these joints. In [8], after computation of an individual's optical flow, each body part that moved considerably while walking, such as the pelvis (including arm movement), legs, upper body, and lower body, was tracked; subsequently, the optical flow of each body part (the length and width of the input of each part were adjusted to 48 \(\times\) 48) was inputted into the NN to enhance performance. In [9], the optical flow of a segment of a complete image was employed instead of purposely capturing images of pedestrians, and the complete displacement of the pedestrian in the image was visible. This method reduced the length and width of the data to 1/8 of the original data for NN input; in addition, four CNNs with different convolution kernel scales were employed. The present study employed the DNN to detect the region where a pedestrian in an image was located. This region was then set as the region of interest (ROI) and its coordinates were sent to the optical flow sequence to capture the ROIs of pedestrians in the corresponding optical flow. In addition, the wide residual networks (Wide ResNet) framework, featuring an exceptional performance and training rate, was employed as the foundation for minor modification. Then, a 3D convolution structure was concatenated at the front to establish a gait feature extraction DNN with optimal representation. This study established a pedestrian recognition method involving pedestrian gait analysis using dense optical flow and pedestrian detection. The flowchart of this method is presented in Figure 1. Items in this flowchart include pedestrian dense optical flow, tracking pedestrian location and capturing pedestrian ROI, the NN that captures high-level abstract features, and a classifier. The first input contained a set of pedestrian color image sequences with n images. Subsequently, two pretraining DNNs, namely YOLOv2 [10] and FlowNet2.0 [11], were applied to track the bounding box of pedestrian location and generate dense optical flow (total of \(\text{n}-1\)) from each pair of two consecutive images. The obtained pedestrian location border was applied to determine the complete pedestrian ROI (PROI). The PROI location data was then applied to the pedestrian optical flow data. Subsequently, the background region where no pedestrian optical flow had been detected was discarded from the captured PROI of the optical flow to reduce the size of the optical flow. Then, the obtained pedestrian optical flow was aligned centrally to facilitate zero-padding in the surrounding area until the maximal border (H length and W width) was reached. The ROIs of the processed pedestrian optical flows were concatenated to form an optical flow sequence of size H \(\times\) W pixels that would be inputted into the DNN to capture high-level abstract  
Figure 1: System flowchart.