## 3 Proposed Methods
### Pre-Processing for Pupil Center Estimation and Tracking
#### 3.1.2 The Used YOLO-Based Models and Training/Validation Process  
The proposed design uses the YOLOv3-tiny deep-learning model. The reasons for choosing a network model based on YOLOv3-tiny are: (1) The YOLOv3-tiny-based model achieves good detection and recognition performance at the same time; (2) The YOLOv3-tiny-based model is effective for small object detection, and the pupil in the nearfield eye image is also a very small object. In addition, because the number of convolutional layers in YOLOv3-tiny is small, the inference model based on YOLOv3-tiny can achieve real-time operations on the software-based embedded platform. In the original YOLOv3-tiny model, as shown in Figure 7, each layer uses three anchor boxes to predict the bounding boxes in a grid cell. However, only one pupil object category needs to be detected in each nearfield eye image, after which the number of anchor boxes required can be reduced. Figure 8 illustrates the network architecture of the YOLOv3-tiny model with only one anchor. Besides that the number of required anchor boxes can be reduced, since the image features of the detected pupil object is not intricate, the number of convolutional layers can also be reduced. Figure 9 shows the network architecture of the YOLOv3-tiny model with one anchor and a one-way path. In Figure 9, since the number of convolutional layers is reduced, the computational complexity can also be reduced effectively. Before the labeled eye images are fed into the YOLOv3-tiny-based deep-learning models, the parameters used for training deep-learning networks must be set properly, e.g., batch, decay, subdivision, momentum, etc., after which the inference models for pupil center tracking will be achieved accurately. In our training design for models, the parameters of Width/height, Channels, Batch, Max_batches, Subdivision, Momentum, Decay, and Learning_rate are set to 416, 3, 64, 500200, 2, 0, 9, 0.0005, and 0.001, respectively. In the architecture of the YOLOv3-tiny model, 13 convolution layers with different filter numbers and pooling layers with different strides are established to allow the YOLOv3-tiny model to have a high enough learning capability for training from images. In addition, the YOLOv3-tiny-based model not only uses the convolution neural network, but also adds upsampling and concrete calculation methods. During the learning process, the YOLO model uses training datasets for training, uses the images in the validation dataset to test the model, observes whether the model is indeed learning correctly, uses the weight back propagation method to gradually converge the weight, and observes whether the weight converges with the loss function. Figure 6: The labeling process for location of the pupil’s ROI. Figure 8: The network architecture of the YOLOv3-tiny model with one anchor. Figure 7: The network architecture of the original YOLOv3-tiny model. Figure 10 depicts the training and testing processes by the YOLO-based models for tracking pupils. In the detection and classification process by the YOLO model, the grid cell, anchor box, and activation function are used to determine the position of the object bounding box in the image. Therefore, the well-trained model can detect the position of the pupil in the image with the bounding box. The detected pupil box can be compared with the ground truth to generate the intersection over union (IoU), precision, and recall values; we use these values to judge the quality of the model training. Table 2 presents the confusion matrix used for the performance evaluation of classification. In pattern recognition for information retrieval and classification, recall is mentioned to as the true positive rate or sensitivity, and precision is mentioned as the positive predictive value. Figure 10: The training and testing processes by the YOLO-based models for tracking pupil. Figure 9: The network architecture of the YOLOv3-tiny model with one anchor and a one-way path. Both precision and recall are based on an understanding and measurement of relevance.