# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 1 Introduction  
Eye-tracking techniques have been gradually applied in different fields, such as remote task of the servo platform motion, which is helpful to achieve accurate manipulation and highly human-machine interaction [1; 2]. Some methods have been implemented to accurately estimate the line-of-sight and output gaze points [3; 4; 5]. When people wear on Head-mounted eye-tracking device, gazes the scene displayed on the Head-mounted display to complete remote tasks. This process contains persons' interaction object and gaze region, and the output gaze points contain different meanings [6; 7]. For visual characteristics, people's gaze attention is uncertain, so that the gaze points have uncertain meanings [8; 9; 10; 11], they use gaze points within a set range and time threshold or with other interaction modes. It is challenging to use eye-tracking gaze points to accurately manipulate the servo platform. Therefore, this paper research the classification of gaze points method from the visual characteristics factor [12; 13; 14], in order to understand the meaning of gaze point to manipulate the servo platform. [PERSON] [10] uses a Clustering method to describe gaze points feature and to recognize user. [PERSON] [11] adopts Support vector machines (SVM) method to recognize the gaze direction. [PERSON] [12] proposes a framework to apply Random Forest Algorithm (RF) for highlighting features that distinguish behavioral differences observed across visual task and understanding gaze behavior. [PERSON] [13] uses Gaussian Hidden Markov Models (GHMM) to analysis gaze and estimate the current proximal intention. [PERSON] [14] relies on Hidden Markov models (HMM) to classify scan-path fixations and inter an observer-related characteristic. [PERSON] [15] proposes Conditional Random Field model (CRF) to classify eye fixation data and [PERSON] [16] uses this model CRF to classify sequence data. The sameBenfold [17] uses CRF method according head motion, walking direction, and appearance to estimate coarse gaze direction. Above all, these research have used different classification and recognition methods to the eye gaze information, but these need more related information to input, and for our application is not clear. [PERSON] [18] fuses the visual saliency and task-dependent in eye-tracking gaze to learn eye attention transfer and predict gaze information. Related studies [19; 20; 21; 22; 23] on gaze point prediction take into account the integration of visual saliency and human gaze attention transfer, that is, the underlying visual characteristics of eye gaze visual characteristics and human brain consciousness attention. The similar research on Named entity recognition studies [24; 25; 26; 27] need to encode input features and output recognition sequences, these are applicated in weapon, product quality and language areas. According to above work, this paper research only has gaze point and gaze image as the input, will need encoding the inputting feature, then understanding the gaze point meaning to classify and recognize using based on CRF model. The clustering method [10] only needs to consider the distance relationship between data by looking for distance iteration of data. The SVM method [11] classifies the data types by looking for a hyperplane of data. Therefore, the proposed method is also compared with these two methods. The RF method [12] build many estimators including all feature to classify. The gaze points observed by the correlated HMM model method [14] are independent of each other, and the labeling at the current moment is only related to the labeling at the previous moment. However, the gaze point recognition often requires more features, and the labeling of the current moment should be related to the previous moment and the next moment. The GHMM method [13] to consider the distribution of features.