scholarly journals An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 25
Author(s):  
Mu-Chun Su ◽  
Tat-Meng U ◽  
Yi-Zeng Hsieh ◽  
Zhe-Fu Yeh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Eye Tracking ◽  
Visual Information ◽  
Deep Neural Network ◽  
Tracking System ◽  
Estimation Algorithms ◽  
Pupil Center ◽  
Tracking Devices ◽  
Medicine Education ◽  
Center Vector

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

A deep neural network based toddler tracking system

2021 ◽  
Author(s):  
Hanife Guney ◽  
Melek Aydin ◽  
Murat Taskiran ◽  
Nihan Kahraman
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Tracking System

An Improved Multi-Pedestrian Tracking System Based on Deep Neural Network

2019 ◽  
Author(s):  
Yuan Gong ◽  
Jianning Chi ◽  
Xiaosheng Yu ◽  
Chengdong Wu ◽  
Yifei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Tracking System ◽  
Pedestrian Tracking

scholarly journals Random Spray Retinex Extensions Considering Region of Interest and Eye Movements

2019 ◽  
Vol 63 (6) ◽  
pp. 60403-1-60403-6
Author(s):  
Midori Tanaka ◽  
Matteo Paolo Lanaro ◽  
Takahiko Horiuchi ◽  
Alessandro Rizzi
Keyword(s):  
Information Processing ◽  
Eye Movements ◽  
Visual Field ◽  
Eye Tracking ◽  
Mathematical Description ◽  
Visual Information ◽  
Tracking System ◽  
Region Of Interest ◽  
Tracking Data ◽  
Proposed Model

Abstract The Random spray Retinex (RSR) algorithm was developed by taking into consideration the mathematical description of Milano-Retinex. The RSR substituted random paths with random sprays. Mimicking some characteristics of the human visual system (HVS), this article proposes two variants of RSR adding a mechanism of region of interest (ROI). In the first proposed model, a cone distribution based on anatomical data is considered as ROI. In the second model, the visual resolution depending on the visual field based on the knowledge of visual information processing is considered as ROI. We have measured actual eye movements using an eye-tracking system. By using the eye-tracking data, we have simulated the HVS using test images. Results show an interesting qualitative computation of the appearance of the processed area around real gaze points.

scholarly journals Detection-Based Object Tracking Applied to Remote Ship Inspection

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 761
Author(s):  
Jing Xie ◽  
Erik Stensrud ◽  
Torbjørn Skramstad
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Tracking System ◽  
Simple Manner ◽  
Detection Model ◽  
Art Object ◽  
Digital Infrastructure ◽  
Inspection Process ◽  
Two Stages

We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.

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