Preliminary study of pupil detection and tracking with low cost optical flow sensors

2012 ◽  
Author(s):  
M. Tresanchez ◽  
D. Font ◽  
M. Teixido ◽  
T. Palleja ◽  
J. Palacin
Keyword(s):  
Optical Flow ◽  
Low Cost ◽  
Flow Sensors ◽  
Detection And Tracking ◽  
Pupil Detection ◽  
Preliminary Study

scholarly journals Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 885
Author(s):  
Yoanda Alim Syahbana ◽  
Yokota Yasunari ◽  
Morita Hiroyuki ◽  
Aoki Mitsuhiro ◽  
Suzuki Kanade ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Hough Transform ◽  
University Hospital ◽  
Transform Method ◽  
Data Set ◽  
Medical Specialists ◽  
Detection And Tracking ◽  
Mexican Hat ◽  
Pupil Detection ◽  
In The Wild

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

scholarly journals Feature detection and tracking in optical flow on non-flat manifolds

2011 ◽  
Vol 32 (15) ◽  
pp. 2047-2052 ◽  
Author(s):  
Sheraz Khan ◽  
Julien Lefevre ◽  
Habib Ammari ◽  
Sylvain Baillet
Keyword(s):  
Optical Flow ◽  
Feature Detection ◽  
Detection And Tracking ◽  
Feature Detection And Tracking ◽  
Flat Manifolds

scholarly journals MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Muhamad Soleh ◽  
Grafika Jati ◽  
Muhammad Hafizhuddin Hilman
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Optical Flow ◽  
Motion Blur ◽  
Research Topic ◽  
Vehicle Tracking ◽  
Flow Density ◽  
Illumination Change ◽  
Dynamic Background ◽  
Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

2018 ◽  
Vol 06 (04) ◽  
pp. 267-275
Author(s):  
Ajay Shankar ◽  
Mayank Vatsa ◽  
P. B. Sujit
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Support Vector ◽  
Svm Classifier ◽  
Range Finding ◽  
Image Patches ◽  
Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

scholarly journals Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chong Shen ◽  
Zesen Bai ◽  
Huiliang Cao ◽  
Ke Xu ◽  
Chenguang Wang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Low Cost ◽  
Integrated Navigation ◽  
Static State ◽  
Integrated Navigation System ◽  
Microaerial Vehicles ◽  
Flow Information ◽  
Optical Flow Sensor ◽  
Navigation Scheme

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

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