Focus on Area Tracking Based on Deep Learning for Multiple Users

Chen, Huihui; Hwang, Bor‐Jiunn; Hsu, Wang-Hsin; Kao, Chiao-Wen; Chen, Wei‐Ting

doi:10.1109/access.2019.2956953

Cited by 1 publication

(1 citation statement)

References 20 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, due to the development of machine learning technology, many studies have used deep learning training methods to improve the accuracy of gaze point estimation [4,5]. Since the Convolutional Neural Network (CNN) excels in many computer vision tasks, it is also used for gaze point estimation [2,6,7]. For CNN-based gaze point estimation, the image is selected as the training and predictive data with the extracted spatial features.…”

Section: Introductionmentioning

confidence: 99%

Gaze Tracking Based on Concatenating Spatial-Temporal Features

Hwang

Chen

Hsieh

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.

show abstract

Section: Introductionmentioning

confidence: 99%