Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors
The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of se...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/14/8/15203 |
| _version_ | 1798026668658393088 |
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| author | Jorge Fernández-Berni Ricardo Carmona-Galán Rocío del Río Richard Kleihorst Wilfried Philips Ángel Rodríguez-Vázquez |
| author_facet | Jorge Fernández-Berni Ricardo Carmona-Galán Rocío del Río Richard Kleihorst Wilfried Philips Ángel Rodríguez-Vázquez |
| author_sort | Jorge Fernández-Berni |
| collection | DOAJ |
| description | The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects. |
| first_indexed | 2024-04-11T18:38:52Z |
| format | Article |
| id | doaj.art-af1d2d37a1f3437ab48bbb3d53b55e20 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:38:52Z |
| publishDate | 2014-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-af1d2d37a1f3437ab48bbb3d53b55e202022-12-22T04:09:04ZengMDPI AGSensors1424-82202014-08-01148152031522610.3390/s140815203s140815203Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual SensorsJorge Fernández-Berni0Ricardo Carmona-Galán1Rocío del Río2Richard Kleihorst3Wilfried Philips4Ángel Rodríguez-Vázquez5Institute of Microelectronics of Seville (IMSE-CNM), CSIC-Univ. Sevilla, calle Américo Vespucio s/n, Seville 41092, SpainInstitute of Microelectronics of Seville (IMSE-CNM), CSIC-Univ. Sevilla, calle Américo Vespucio s/n, Seville 41092, SpainInstitute of Microelectronics of Seville (IMSE-CNM), CSIC-Univ. Sevilla, calle Américo Vespucio s/n, Seville 41092, SpainGhent University/iMinds/TELIN-IPI, St-Pietersnieuwstraat 41, Ghent B-9000, BelgiumGhent University/iMinds/TELIN-IPI, St-Pietersnieuwstraat 41, Ghent B-9000, BelgiumInstitute of Microelectronics of Seville (IMSE-CNM), CSIC-Univ. Sevilla, calle Américo Vespucio s/n, Seville 41092, SpainThe capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.http://www.mdpi.com/1424-8220/14/8/15203visual sensor networksInternet of Things (IoT)privacysecurityvision sensorfocal-plane processingobfuscationpixelationgranular spacefeature extraction |
| spellingShingle | Jorge Fernández-Berni Ricardo Carmona-Galán Rocío del Río Richard Kleihorst Wilfried Philips Ángel Rodríguez-Vázquez Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors Sensors visual sensor networks Internet of Things (IoT) privacy security vision sensor focal-plane processing obfuscation pixelation granular space feature extraction |
| title | Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors |
| title_full | Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors |
| title_fullStr | Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors |
| title_full_unstemmed | Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors |
| title_short | Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors |
| title_sort | focal plane sensing processing a power efficient approach for the implementation of privacy aware networked visual sensors |
| topic | visual sensor networks Internet of Things (IoT) privacy security vision sensor focal-plane processing obfuscation pixelation granular space feature extraction |
| url | http://www.mdpi.com/1424-8220/14/8/15203 |
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