Cooperative image analysis in visual sensor networks

Redondi, Alessandro E. C.; Cesana, Matteo; Tagliasacchi, Marco; Filippini, Ilario; Dán, György; Fodor, Viktória

doi:10.1016/j.adhoc.2015.01.008

Cited by 22 publications

(13 citation statements)

References 31 publications

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“…Our paper is different from these works as it provides a characterization of a variety of statistical properties of interest points that are relevant for in-network processing is VSNs. While our study is motivated by applications in VSNs [19]- [21], our results may provide insight into parallel or distributed processing of visual features on multi-core computing platforms and for stream processing in cloud environments [22], [23]. Furthermore, the evaluation and the comparison of the spatial distributions of the interest points using SURF and BRISK are also relevant for vi-sual tasks such as homography estimation, 3D visualization and tracking, as demonstrated in [17].…”

Section: Introductionmentioning

confidence: 98%

Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

Dán

Khan

Fodor

2015

IEEE Trans. Multimedia

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We study the statistical characteristics of SURF and BRISK interest points and descriptors, with the aim of supporting the design of distributed processing across sensor nodes in a resource-constrained visual sensor network (VSN). Our results show high variability in the density, the spatial distribution, and the octave layer distribution of the interest points. The high variability implies that balancing the processing load among the sensor nodes is a very challenging task, and obtaining a priori information is essential, e.g., through prediction. Our results show that if a priori information is available about the images, then Topinterest point selection, limited, octave-based processing at the camera node, together with area-based interest point detection and extraction at the processing nodes, can balance the processing load and limit the transmission cost in the network. Complete interest point detection at the camera node with optimized descriptor extraction delegation to the processing nodes in turn can further decrease the transmission load and allow a better balance of the processing load among the network nodes. Index Terms-BRISK, distributed feature extraction, interest point distribution, SURF, visual sensor network (VSN).1520-9210

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Section: Introductionmentioning

confidence: 98%

Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

Dán

Khan

Fodor

2015

IEEE Trans. Multimedia

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“…Similarly, Redondi et al [81,82] propose a framework for cooperative feature extraction on low-power visual sensor nodes. Several different network configurations and protocols are proposed and empirically evaluated in terms of speed up of feature extraction task, network lifetime, and energy consumption.…”

Section: V I S U a L F E A T U R E T R A N S -M I S S I O N A N Dmentioning

confidence: 99%

“…Global encoding Bag-of-Words (BoW) [5], Pyramid Kernel [60], Tree codebook [61], Kernel codebook (KC) [62], Sparse coding [63], Locality-constrained Linear Coding (LLC) [64], Hamming Embedding (HE) [65], VLAD [66], Fisher Kernel (FK) [67], Super Vector [68], Bag-of-Binary-Words [69], BVLAD [70] Other Location coding [71,72], SIFT-Preserving JPEG [73] and H.264/AVC [74], Chen and Moulin [75], Hybrid ATC (HATC) [7], Interframe patch [9] and descriptor [76] coding, VideoSIFT [10], VideoBRISK [77] Feature networking Section V -Y a n g et al [78,79], feature extraction offloading [80][81][82], lossy feature transmission [3], Mobile Visual Search [83] exploited to encode visual features, providing a significant coding gain with respect to the case of still images. Similar works in the previous literatures focus on either feature extraction [12,13] or encoding [14,15].…”

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confidence: 99%

A survey on compact features for visual content analysis

Baroffio

Redondi

Tagliasacchi

et al. 2016

SIP

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“…In [15] we provided closed form expressions for the minimization of the completion time of distributed feature extraction of a single image.…”

Section: Feature Networkingmentioning

confidence: 99%

GreenEyes: Networked energy-aware visual analysis

Baroffio

Cesana

Redondi

et al. 2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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The GreenEyes project aims at developing a comprehensive set of new methodologies, practical algorithms and protocols, to empower wireless sensor networks with vision capabilities. The key tenet of this research is that most visual analysis tasks can be carried out based on a succinct representation of the image, which entails both global and local features, while it disregards the underlying pixel-level representation. Specifically, GreenEyes will pursue the following goals: i) energy-constrained extraction of visual features; ii) rate-efficiency modelling and coding of visual feature; iii) networking streams of visual features. This will have a significant impact on several scenarios including, e.g., smart cities and environmental monitoring.

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Cooperative image analysis in visual sensor networks

Cited by 22 publications

References 31 publications

Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

Characterization of SURF and BRISK Interest Point Distribution for Distributed Feature Extraction in Visual Sensor Networks

A survey on compact features for visual content analysis

GreenEyes: Networked energy-aware visual analysis

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