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Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
[article]
2020
arXiv
pre-print
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The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. ...
In this paper, we initiate the first step to comprehensively investigate the potential hazards of the blur effect for DNN, caused by object motion. ...
Acknowledgments and Disclosure of Funding ...
arXiv:2002.03500v3
fatcat:tsfdhjbhiff5fno3qgiq4usymy
"Watch Your Step": Precise Obstacle Detection and Navigation for Mobile Users through their Mobile Service
2019
IEEE Access
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Obstacle detection or navigation is useful for mobile users, especially for the visually impaired. ...
On the basis of the experimental results, we discuss the implications for the design of the system. INDEX TERMS Google Tango, navigation, visually impaired, obstacle detection. ...
Moreover, an audio warning would say, ''Watch out! Obstacle detected, please turn left/right,'' thereby pointing out the safe way for users to follow. ...
doi:10.1109/access.2019.2915552
fatcat:s7yxrzl2jva3hkcn6mrzc5zafu
Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain
[article]
2023
arXiv
pre-print
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Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers ...
At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network ...
If edge servers perform storage and processing tasks for users, there is no need to send large amounts of raw data deep into the network. ...
arXiv:2301.01221v2
fatcat:njzop26brrculbncsuxhhry6c4
Machine learning architectures to predict motion sickness using a Virtual Reality rollercoaster simulation tool
[article]
2018
arXiv
pre-print
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Machine learning architectures based on deep neural networks are trained using this data aiming to predict motion sickness levels. ...
This paper describes a novel framework to get automated ratings on motion sickness using Neural Networks. ...
They experience physically induced motion sickness as well as blurred vision and headaches [3] . ...
arXiv:1811.01106v1
fatcat:zv4wpkzur5gmrdckjvbh2vbyvi
Zoom In, Zoom Out, Refocus: is a Global Electronic Literature Possible?
2017
Hyperrhiz
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For instance, a package for Dungeon Keeper lists four key features of the game, out of which the first two concern control over the camera: "switch your perspective," "rotate your view," "take on your ...
AI engines use a variety of approaches to simulate human intelligence, from rule-based systems to neural networks. ...
doi:10.20415/hyp/016.e01
fatcat:ad7domr7inb7rlxhuh2rx7ekwe
Is current research on adversarial robustness addressing the right problem?
[article]
2022
arXiv
pre-print
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Only then we may be able to solve the problem of adversarial vulnerability. ...
Maybe instead of narrowing down on imperceptible adversarial perturbations, we should attack a more general problem which is finding architectures that are simultaneously robust to perceptible perturbations ...
My principal focus is on deep neural networks (DNNs) for vision, however, a lot of the arguments also apply to other domains. ...
arXiv:2208.00539v2
fatcat:m5mwg4pdpnfjxaajosw6yovj7m
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
[article]
2020
arXiv
pre-print
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In this primer we review the budding field of motion capture with deep learning. ...
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. ...
Acknowledgments: We thank Yash Sharma for discussions around future directions in self-supervised learning, Erin Diel, Maxime Vidal, Claudio Michaelis, Thomas Biasi for comments on the manuscript. ...
arXiv:2009.00564v2
fatcat:w22iv453cbaa5fidf5hwemcxeu
Bird Species Identification Using Yolact Classifier
2021
Bioscience Biotechnology Research Communications
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Capturing a perfect shot for an ornithologist is a challenging task when a bird is distant or the image captured is blurred or not recognizable due to motion, geographical, or weather phenomenon. ...
The model creates pseudo masks at a rate of 34 fps from features extracted and predicts the class of the species combining all the pseudo masks and comparing the features from the train data. ...
If you are interested in how to train your classification or object recognition model, there is a great article about Deep Learning in Computer Vision, which python calls Deep Learning for Computer Vision ...
doi:10.21786/bbrc/14.9.23
fatcat:ro46j6welncr5kvaipg775dfci
Soccer on Your Tabletop
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. ...
From a YouTube video of a soccer game, our system outputs a dynamic 3D reconstruction of the game, that can be viewed interactively on your tabletop with an Augmented Reality device. ...
Acknowledgements This work is supported by NSF/Intel Visual and Experimental Computing Award #1538618 and the UW Reality Lab. ...
doi:10.1109/cvpr.2018.00498
dblp:conf/cvpr/RematasKCS18
fatcat:e4fd5fftjjcchdelzbmlqtmclu
Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene
2022
Security and Communication Networks
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On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling ...
Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. ...
RNN is a deep neural network that uses context state to mine time series information in data. ...
doi:10.1155/2022/8202383
fatcat:fxzv3eeiyzgnnc4jc6qid4ln4m
Static Video Compression's Influence on Neural Network Performance
2022
Electronics
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In parallel, a convolutional neural network model is trained to recognise action in the videos and is tested on the compressed videos until the neural network fails to predict the action observed in the ...
The concept of action recognition in smart security heavily relies on deep learning and artificial intelligence to make predictions about actions of humans. ...
The trained 5C3DNN model is further tested on bitrate-altered videos to find out the extent to which we can decrease the bitrate of the video so that the neural network model fails to predict the action ...
doi:10.3390/electronics12010008
fatcat:gdkgqwv2pvhrjdcdukidbbqe7a
A Survey of Unconstrained Face Recognition Algorithm and Its Applications
2016
International Journal of Security and Its Applications
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Face-recognition is becoming common among the section of study in computer-vision, while it is also one of the very effective programs of comprehension and image-analysis. ...
In this paper, we discuss the different face recognition techniques and find a better method for pose variation, nonuniform motion blur and Illumination by using a Reference face graph for face recognition ...
An example of the neural classifier is the Probabilistic Selection-centered Neural Network (PDNN) (Lin, 1997) . PDNN does not have the entirely connected topology. ...
doi:10.14257/ijsia.2016.10.12.30
fatcat:ygzrwiskmjabfcclaxc4qdpi7a
A Light Stage on Every Desk
[article]
2021
arXiv
pre-print
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Every time you sit in front of a TV or monitor, your face is actively illuminated by time-varying patterns of light. ...
We train a deep network on images plus monitor patterns of a given user and learn to predict images of that user under any target illumination (monitor pattern). ...
Our key insight is that the simple act of watching video on a monitor or TV sends patterns of light across your face. ...
arXiv:2105.08051v2
fatcat:ydljcw7osne2vp3ed5z63rihqa
Soccer on Your Tabletop
[article]
2018
arXiv
pre-print
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At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. ...
We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage. ...
Acknowledgements This work is supported by NSF/Intel Visual and Experimental Computing Award #1538618 and the UW Reality Lab. ...
arXiv:1806.00890v1
fatcat:kjjd4exk3ngytpxtm7m33e37zu
IoT+Small Data: Transforming in-store shopping analytics & services
2016
2016 8th International Conference on Communication Systems and Networks (COMSNETS)
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We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time ...
the exact item picked (via analysis of the smartwatch camera data). ...
convolutional neural networks. ...
doi:10.1109/comsnets.2016.7439946
dblp:conf/comsnets/RadhakrishnanSS16
fatcat:umzn5rnya5gcjahuepikhctvi4
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