Querying geo-tagged videos for vision applications using spatial metadata

Cai, Yaqi; Lü, Ying; Kim, Seon Ho; Nocera, Luciano; Shahabi, Cyrus

doi:10.1186/s13640-017-0165-6

Cited by 4 publications

(1 citation statement)

References 30 publications

(55 reference statements)

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“…MBTR (minimum-boundary-tilt rectangles) in leaf nodes effectively represent the motion scene in the index [24]. A novel geospatial image and video filtering tool (GIFT) was proposed in [25]. Ay et al [26] proposed a new method of video query.…”

Section: Geo-tagged Videosmentioning

confidence: 99%

Discovering Homogeneous Groups from Geo-Tagged Videos

Lew

Nam

2023

Sensors

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The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.

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Section: Geo-tagged Videosmentioning

confidence: 99%

Discovering Homogeneous Groups from Geo-Tagged Videos

Lew

Nam

2023

Sensors

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RETRACTED ARTICLE: IoT using machine learning security enhancement in video steganography allocation for Raspberry Pi

Karthika

Saraswathi

2020

J Ambient Intell Human Comput

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A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-Metadata

Chaudhuri

2019

ACM Trans. Multimedia Comput. Commun. Appl.

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Events such as live concerts, protest marches, and exhibitions are often video recorded by many people at the same time, typically using smartphone devices. In this work, we address the problem of geo-localizing such events from crowd-generated data. Traditional approaches for solving such a problem using multiple video sequences of the event would require highly complex computer vision (CV) methods, which are computation intensive and are not robust under the environment where visual data are collected through crowd-sourced medium. In the present work, we approach the problem in a probabilistic framework using only the sensor metadata obtained from smartphones. We model the event location and camera locations and orientations (camera parameters) as the hidden states in a Hidden Markov Model. The sensor metadata from GPS and the digital compass from user smartphones are used as the observations associated with the hidden states of the model. We have used a suitable potential function to capture the complex interaction between the hidden states (i.e., event location and camera parameters). The non-Gaussian densities involved in the model, such as the potential function involving hidden states, make the maximum-likelihood estimation intractable. We propose a pseudo-likelihood-based approach to maximize the approximate-likelihood, which provides a tractable solution to the problem. The experimental results on the simulated as well as real data show correct event geo-localization using the proposed method. When compared with several baselines the proposed method shows a superior performance. The overall computation time required is much smaller, since only the sensor metadata are used instead of visual data.

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Querying geo-tagged videos for vision applications using spatial metadata

Cited by 4 publications

References 30 publications

Discovering Homogeneous Groups from Geo-Tagged Videos

Discovering Homogeneous Groups from Geo-Tagged Videos

RETRACTED ARTICLE: IoT using machine learning security enhancement in video steganography allocation for Raspberry Pi

A Pseudo-likelihood Approach for Geo-localization of Events from Crowd-sourced Sensor-Metadata

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