Triangulation Positioning by Means of Wi-Fi Signals in Indoor Conditions

Korogodir, Ilya V.; Dneprov, Vladimir V.; Mikhaylova, Olga K.

doi:10.1109/piers-spring46901.2019.9017863

Cited by 5 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative approach to indoor location identification based on using WiFi router access points [5]. There are various approaches that can be utilised to calculate a target device's location using distance measurements from the APs.…”

Section: Wifi Device Location Identificationmentioning

confidence: 99%

Object Tracking using Modified Hexagonal Search Algorithm

Ghoul,

Zaidi,

Laboudi

2024

icisna

View full text Add to dashboard Cite

Object tracking is one of the most important problems in computer vision, so many methods have been proposed to solve it. The principle of object tracking mainly relies on motion estimation to track the motion of objects. In motion estimation, a variety of algorithms based on block matching have been proposed in order to address diverse issues such as reducing the number of search points, computational complexity. Although existing methods for object tracking provide good results, most of them face performance issues related to computation time. In this work, we propose a modified hexagonal search algorithm (MHS) to improve computation time of estimated motion while preserving efficiency. The proposed algorithm proceeds in three steps. In the first step, a small hexagonal pattern is used to find the smaller motion vectors. In the second step, the large hexagonal pattern is used to determine the direction of motion. In the third step, the small hexagonal search pattern is used to find the final solution. The MHS algorithm is used within the object tracking system after performing the object detection step. To validate our proposal, we consider several video sequences. The experimental results show that MHS outperforms some related works

show abstract

Section: Wifi Device Location Identificationmentioning

confidence: 99%