Context Awareness and Step Length Estimation by Shape Distance and H-Features

Kim, Dae Hyun; Lee, Yonghyeon; Park, Chan Gook

doi:10.1007/s12555-019-0725-x

Cited by 4 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the eye-in-hand configuration was used in this paper. The eye-in-hand configuration is a posture the camera takes when mounted on a manipulator and it can either be after or before the wrist of the robotic arm [23], [24]. Figure 2 shows the Pixy2 camera mounted on the robot manipulator that is used for a pick and place purpose.…”

Section: Camera Set-upmentioning

confidence: 99%

Object distance estimation using a monovision camera

Idohen,

Afolayan,

Umar

2023

IJRA

View full text Add to dashboard Cite

In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object’s distance to complete the 3D information needed to determine the object’s location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. The distances measured in the experiment with a set of training data obtained from the image were fitted into a curve using the least-square framework to derive a non-linear function that was used for estimating the object’s distance also known as the z-coordinate. The result from the experiment showed that there was an average error of 1.33 mm between the actual distance and the estimated distance of the object. Hence, this method can be applied in many robotic and autonomous systems applications.

show abstract

Section: Camera Set-upmentioning

confidence: 99%

Object distance estimation using a monovision camera

Idohen,

Afolayan,

Umar

2023

IJRA

View full text Add to dashboard Cite

show abstract

“…where Y is original value and Y is rescaled/normalized value of the feature in the said range. These rescaled features are further employed in multiclass classifiers for training and testing the dataset using DT, DA, SVM, kNN (Zhu et al, 2019;Ali and Park, 2020;Kim et al, 2020;Li et al, 2020), and ensemble classifiers. For better classification of samples in the respective classes, the value of k is set to 1 in the kNN classifier.…”

Section: Feature Space and Classificationmentioning

confidence: 99%

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface

Arif¹,

Khan²,

Naseer³

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

A passive brain–computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

show abstract

Mitigation of a Heading Drift in Pedestrian Dead-reckoning Caused by the Sensor Bandwidth

Lee

Park

2021

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

Context Awareness and Step Length Estimation by Shape Distance and H-Features

Cited by 4 publications

References 23 publications

Object distance estimation using a monovision camera

Object distance estimation using a monovision camera

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface

Mitigation of a Heading Drift in Pedestrian Dead-reckoning Caused by the Sensor Bandwidth

Contact Info

Product

Resources

About