Visibility monitoring using conventional roadside cameras – Emerging applications

Babari, Raouf; Hautiére, Nicolas; Dumont, Éric; Paparoditis, Nicolas; Misener, James A

doi:10.1016/j.trc.2011.11.012

Cited by 56 publications

(43 citation statements)

References 27 publications

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“…Importantly, the OpenAQ system checks each data source for updated information every 10 min, which guarantees that the data will be available from the platform almost immediately after they are published by the original data providers. In Europe, OpenAQ currently provides PM 10 and PM 2.5 data from about 1800 and 800 locations, respectively. The hackAIR data collection framework retrieves up-to-date information from each location by regularly performing appropriate queries to the Representation State Transfer (REST) API provided by OpenAQ.…”

Section: Environmental Data Discovery and Indexingmentioning

confidence: 99%

“…Particulate matter up to 10 micrometers in diameter (PM 10 ) is able to penetrate the bronchi, while particulate matter with diameter up to 2.5 micrometers (PM 2.5 ) can penetrate the lungs and enter the circulatory system [3]. The International Agency for Research on Cancer (IARC) concluded in 2013 that particulate matter is carcinogenic to humans [4].…”

Section: Introductionmentioning

confidence: 99%

“…This approach has been explored in previous research works. For example, Babari et al [10] developed model-driven approaches to monitor and estimate atmospheric visibility distance and air pollution levels through the use of ordinary cameras. Another approach is iSPEX (http://ispex-eu.org/), a low-cost mass-producible optical add-on for smartphones with a corresponding app, which turns a smartphone camera into a spectropolarimeter [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

hackAIR: Towards Raising Awareness about Air Quality in Europe by Developing a Collective Online Platform

Kosmidis

Syropoulou

Tekes

et al. 2018

IJGI

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Abstract:Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist.

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Section: Environmental Data Discovery and Indexingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

hackAIR: Towards Raising Awareness about Air Quality in Europe by Developing a Collective Online Platform

Kosmidis

Syropoulou

Tekes

et al. 2018

IJGI

View full text Add to dashboard Cite

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“…Massively distributed visual sensors (webcams) are utilized for phenology study, scene and environment understanding [19,6]. A multi-tier network SensEye of heterogeneous cameras has been proposed to overcome the disadvantage of singletier networks in surveillance application, performing object detection, recognition and tracking tasks [25].…”

Section: Event Detection Using Visual Sensorsmentioning

confidence: 99%

Tweeting Cameras for Event Detection

Wang

Kankanhalli

2015

Proceedings of the 24th International Conference on World Wide Web

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We are living in a world of big sensor data. Due to the widespread prevalence of visual sensors (e.g. surveillance cameras) and social sensors (e.g. Twitter feeds), many events are implicitly captured in real-time by such heterogeneous "sensors". Combining these two complementary sensor streams can significantly improve the task of event detection and aid in comprehending evolving situations. However, the different characteristics of these social and sensor data make such information fusion for event detection a challenging problem. To tackle this problem, we propose an innovative multi-layer tweeting cameras framework integrating both physical sensors and social sensors to detect various concepts of real-world events. In this framework, visual concept detectors are applied on camera video frames and these concepts can be construed as "camera tweets" posted regularly. These tweets are represented by a unified probabilistic spatio-temporal (PST) data structure which is then aggregated to a concept-based image (Cmage) as the common representation for visualization. To facilitate event analysis, we define a set of operators and analytic functions that can be applied on the PST data by the user to discover occurrences of events and to analyse evolving situations. We further leverage on geo-located social media data by mining current topics discussed on Twitter to obtain the high-level semantic meaning of detected events in images. We quantitatively evaluate our framework with a large-scale dataset containing images from 150 New York real-time traffic CCTV cameras, university foodcourt camera feeds and Twitter data, which demonstrates the feasibility and effectiveness of our proposed framework. Results of combining camera tweets and social tweets are shown to be promising for detecting real-world events.

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“…[14] proposed to use a homomorphic filter or a Haar function in addition to the high-pass filter in order to reduce the effects of nonuniform illumination. [3] proposed a visibility descriptor which is robust to illumination variations by making use of Lambertian surfaces only. However, the method to select the Lambertian surfaces is superfificially justified.…”

Section: Introductionmentioning

confidence: 99%