Dijkstra Algorithm Application: Shortest Distance  between Buildings

Deepa, G.; Kumar, Priyank V.; Manimaran, A.; Rajakumar, K.; Krishnamoorthy, V.

doi:10.14419/ijet.v7i4.10.26638

Cited by 12 publications

(8 citation statements)

References 1 publication

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“…These extracted features are then compared with the training features to determine the current location. To find the shortest path from the starting point to the destination, we employ the A* algorithm [12].…”

Section: Related Workmentioning

confidence: 99%

Real-time indoor tracking for augmented reality using computer vision technique

Shewail,

H. Zayed,

A. M. Elsayed

2024

IJ-AI

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In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces

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Section: Related Workmentioning

confidence: 99%

Real-time indoor tracking for augmented reality using computer vision technique

Shewail,

H. Zayed,

A. M. Elsayed

2024

IJ-AI

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“…The KNN nearest neighbor algorithm was used to remove unnecessary features in an earlier trained module for digit recognition [68]- [70], and determine the user's initial location positioning [71], [72]. A* search algorithm [73], [74], determines the shortest route from the initial point to the destination and suggests it to the users [75], [76]. Through indoor navigation, users can use AR electronic bulletin boards at a particular location to present relevant information about the location [77].…”

Section: Hybrid Trackingmentioning

confidence: 99%

Survey of indoor tracking systems using augmented reality

Shewail

Elsayed²,

Zayed³

2023

IJ-AI

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<p><span lang="EN-US">Augmented reality overlays virtual content on the physical world, displaying location-based information more efficiently. Tracking is a trace detail of the location recorded, either by taking a reading based on a set time interval, set distance, and change in direction by more than a certain angle, or a combination of these. Tracking is divided into two types, outdoor tracking, and indoor tracking. Augmented reality added value and information to tracking applications, whether indoor or outdoor. Recently, outdoor tracking by the global positioning system (GPS) became an essential component in navigation applications. However, indoor tracking is still challenging in the augmented reality field. Most augmented reality indoor tracking use optical and sensor-based tracking, such as markers, beacons, Raspberry Pi, and route planner modules for the building. With the technology enhancement, indoor tracking started to rely on WiFi, Bluetooth, geographic information system, radiofrequency identification, and sensor chip technologies. Our paper describes the recent augmented reality tracking techniques from 2011 to 2021. The paper compares image detection algorithms with various communication technologies, disscusing the advantages and the limitations of each technology.</span></p>

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“…It is noted that when the problem size increases, the Dijkstra's algorithm requires longer time to solve the problem. There is an unlimited application using Dijkstra's algorithm to find the shortest path especially in a single source problem [18], [19]. In other work, the large computation has been decreased and the efficiency has been increased by a constrained optimization condition applied to the Dijkstra's algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

Ata

Soh

Aris

et al. 2021

IJEECS

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A common algorithm to solve the single-source shortest path (SSSP) is the Dijkstra algorithm. However, the traditional Dijkstra’s is not accurate and need more time to perform the path in order it should visit all the nodes in the graph. In this paper, the Dijkstra-ant colony algorithm (ACO) with binary search tree (BST) has been proposed. Dijkstra and ACO are integrated to produce the smart guidance algorithm for the indoor parking system. Dijkstra algorithm initials the paths to finding the shortest path while ACO optimizes the paths. BST has been used to store the paths that Dijkstra algorithm initialled. The proposed algorithm is aimed to control the shortest path as well as guide the driver towards the nearest vacant available space near the entrance. This solution depending on applying the optimization on an optimal path while the traditional ACO is optimizing the random path based on the greedy algorithm hence we get the most optimal path. Moreover, the reason behind using the BST is to make the generation of the path by Dijkstra’s algorithm more accurate and less time performance. The results show a range of 8.3% to 26.8% improvement in the proposed path compared to the traditional Dijkstra’s algorithm.

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Dijkstra Algorithm Application: Shortest Distance between Buildings

Cited by 12 publications

References 1 publication

Real-time indoor tracking for augmented reality using computer vision technique

Real-time indoor tracking for augmented reality using computer vision technique

Survey of indoor tracking systems using augmented reality

Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

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