Improving a vision indoor localization system by a saliency-guided detection

Elloumi, Wael; Guissous, Kamel; Chetouani, Aladine; Treuillet, Sylvie

doi:10.1109/vcip.2014.7051526

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…The first line of Table 8 is a good example, as it shows that running the localization algorithm with a database of 1000 frames was eight times faster than with a database of 8000 frames. To reduce the processing time, Elloumi et al [87] limited the similarity search of two images to only a selection of areas within the images, thus reducing the number of features by 40%. These areas were considered to contain the most important characteristics and were selected based on a metric that combined orientation, color, intensity, flickering effects, and motion.…”

Section: Indoor Localization Solutions With 2d Cameras + Other Sensor...mentioning

confidence: 99%

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

111

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Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

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Section: Indoor Localization Solutions With 2d Cameras + Other Sensor...mentioning

confidence: 99%

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

111

View full text Add to dashboard Cite

show abstract

“…Visual attention plays an important role in the image analysis domain and is exploited in several applications (image retrieval [28], indoor localization [29] and so on). It drives our visual understanding by focusing on regions, so-called salient regions, that represent the more perceptually attractive zones in the image.…”

Section: Saliency Extractionmentioning

confidence: 99%

On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

Chetouani

2020

Signal Processing: Image Communication

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“…], [1], etc. ), remote sensing [30], watermarking [34], map viewing [51] [5], indoor localization [29], perception [14], image enhancement [12], [19], healthcare [38] among many others.…”

Section: Introductionmentioning

confidence: 99%

SATSal: A Multi-Level Self-Attention Based Architecture for Visual Saliency Prediction

et al. 2022

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Human visual Attention modelling is a persistent interdisciplinary research challenge, gaining new interest in recent years mainly due to the latest developments in deep learning. That is particularly evident in saliency benchmarks. Novel deep learning-based visual saliency models show promising results in capturing high-level (top-down) human visual attention processes. Therefore, they strongly differ from the earlier approaches, mainly characterised by low-level (bottom-up) visual features. These developments account for innate human selectivity mechanisms that are reliant on both high-and low-level factors. Moreover, the two factors interact with each other. Motivated by the importance of these interactions, in this project, we tackle visual saliency modelling holistically, examining if we could consider both high-and low-level features that govern human attention. Specifically, we propose a novel method SAtSal (Self-Attention Saliency). SAtSal leverages both high and low-level features using a multilevel merging of skip connections during the decoding stage. Consequently, we incorporate convolutional self-attention modules on skip connection from the encoder to the decoder network to properly integrate the valuable signals from multilevel spatial features. Thus, the self-attention modules learn to filter out the latent representation of the salient regions from the other irrelevant information in an embedded and joint manner with the main encoder-decoder model backbone. Finally, we evaluate SAtSal against various existing solutions to validate our approach, using the well-known standard saliency benchmark MIT300. To further examine SAtSal's robustness on other image types, we also evaluate it on the Le-Meur saliency painting benchmark.

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Improving a vision indoor localization system by a saliency-guided detection

Cited by 14 publications

References 14 publications

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

SATSal: A Multi-Level Self-Attention Based Architecture for Visual Saliency Prediction

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