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Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked visual sensors

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sensorS 2014年第8期14卷 15203-15226页

作者： Fernandez-Berni, Jorge Carmona-Galan, Ricardo del Rio, Rocio Kleihorst, Richard Philips, Wilfried Rodriguez-Vazquez, Angel CSIC Univ Sevilla Inst Microelect Seville IMSE CNM Seville 41092 Spain Ghent Univ IMinds TELIN IPI B-9000 Ghent Belgium

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.

关键词： visual sensor networks Internet of Things (IoT) privacy security vision sensor focal-plane processing obfuscation pixelation granular space feature extraction

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Incremental Structured Dictionary Learning for Video sensor-Based Object Tracking

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sensorS 2014年第2期14卷 3130-3155页

作者： Xue, Ming Yang, Hua Zheng, Shibao Zhou, Yi Yu, Zhenghua Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200240 Peoples R China Shanghai Key Lab Digital Media Proc & Transmiss Shanghai 200240 Peoples R China Dalian Maritime Univ Dept Elect Engn Dalian 116026 Peoples R China Bocom Smart Network Technol Inc Shanghai 200233 Peoples R China

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

关键词： appearance model object tracking sparse representation structured dictionary learning Bayesian inference visual sensor networks

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Nodes Placement for Optimizing Coverage of visual sensor networks

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10th Pacific Rim Conference on Multimedia

作者： Wang, Chang Qi, Fei Shi, Guang-Ming Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9783642104664

visual sensor networks have become a research focus with its expanding application domains How to achieve optimal coverage to improve visual network's capability of obtaining regional information is a critical issue As a visual sensor has a bounded field of view, a random deployment of network sensors can hardly solve this issue This paper proposes a bounded observation field sensing model based on the sensing feature of visual sensor According to this model, a sensor placement method is devised by means of multi-agent genetic algorithm (MAGA) The positions and poses of sensors which can enhance the coverage can be effectively worked out by this placement algorithm. thus the visual network's capability of obtaining regional in can be improved Experiment results show that the algorithm proposed is effective in both 2D and 3D scenes

关键词： visual sensor networks bounded observation field sensing model optimal coverage sensor placement

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A Weighted Compressive Sensing Method for Multi-view Images 5

A Weighted Compressive Sensing Method for Multi-view Images

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5th International Conference on Information and Communication Technology Convergence (ICTC)

作者： Lee, Hyungkeuk Lee, Hyun-Woo ETRI Media Networking Sect Daejeon South Korea

ISBN: (纸本)9781479967865

Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: 'compressive sampling' or 'compressive sensing' at rates smaller than the Nyquist sampling rate. while theoretical studies have demonstrated the stability of CS, specific examples of successful and practical applications remain elusive. In this paper, we apply multi-view images obtained from multiple visual sensor nodes to CS, where the measurement side emphasizes 'important' CS data in view of 3D reconstruction. Due to the spatial proximity of cameras, the obtained images have high correlations. However, cameras cannot collaborate in image acquisition. Therefore, compression should be performed locally at each camera and reconstruction is executed jointly to consider dependencies in the acquired data.

关键词： compressive sensing multi-veiw image visual sensor networks weighted compressive sensing 3d reconstruction

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CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING 22

CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING

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22nd European Signal Processing Conference (EUSIPCO)

作者： Monteiro, Pedro Ascenso, Joao Inst Telecomunicacoes Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9780992862619

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor;and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task.

关键词： Local features feature coding binary descriptors mode decision visual sensor networks

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Hierarchical Feature Scheme for Object Recognition in visual sensor networks

ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW

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ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW 2009年第1-2期76卷 38-44页

作者： Sulic, Vildana Pers, Janez Kristan, Matej Kovacic, Stanislav Univ Ljubljani Fak Elektrotehn Trzaska 25 Ljubljana 1000 Slovenia

visual sensor networks are the meeting point of two significantly different technologies: one is image processing with high computation and storage demands, and the other is distributed sensor approach with low power, low computational and storage capabilities. We propose a framework for hierarchical feature encoding scheme for a frequent computer vision task - object recognition. The key of our approach is the principle that individual nodes in the network hold only a small amount of information about objects seen by the network. However, this information is sufficient to efficiently route network query, when a new, unknown object is encountered. A set of criteria has to be fulfilled by the object recognition method to qualify for use in our framework. The paper provides examples of three widely known object recognition approaches that can be easily adapted for use in such hierarchical feature encoding scheme.

关键词： visual sensor networks computer vision object recognition

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LOCAL FEATURE SELECTION FOR EFFICIENT BINARY DESCRIPTOR CODING

LOCAL FEATURE SELECTION FOR EFFICIENT BINARY DESCRIPTOR CODI...

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IEEE International Conference on Image Processing

作者： Pedro Monteiro Joao Ascenso Fernando Pereira Instituto Superior Tecnico - Instituto de Telecomunicacoes

ISBN: (纸本)9781479957521

In a visual sensor network, a large number of camera nodes are able to acquire and process image data locally, collaborate with other camera nodes and provide a description about the captured events. Typically, camera nodes have severe constraints in terms of energy, bandwidth resources and processing capabilities. Considering these unique characteristics, coding and transmission of the pixel-level representation of the visual scene must be avoided, due to the energy resources required. A promising approach is to extract at the camera nodes, compact visual features that are coded to meet the bandwidth and power requirements of the underlying network and devices. Since the total number of features extracted from an image may be rather significant, this paper proposes a novel method to select the most relevant features before the actual coding process. The solution relies on a score that estimates the accuracy of each local feature. Then, local features are ranked and only the most relevant features are coded and transmitted. The selected features must maximize the efficiency of the image analysis task but also minimize the required computational and transmission resources. Experimental results show that higher efficiency is achieved when compared to the previous state-of-the-art.

关键词： visual sensor networks Local features Binary descriptors Descriptors selection Binary descriptor coding

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BRISKOLA: BRISK OPTIMIZED FOR LOW-POWER ARM ARCHITECTURES

BRISKOLA: BRISK OPTIMIZED FOR LOW-POWER ARM ARCHITECTURES

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IEEE International Conference on Image Processing

作者： Luca Baroffio Antonio Canclini Matteo Cesana Alessandro Redondi Marco Tagliasacchi Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria

ISBN: (纸本)9781479957521

Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or visual sensor networks (VSN). In this work, we dissect and analyze BRISK, a state-of-the-art low-power visual feature extractor, in order to evaluate the impact of its individual building blocks on the overall energy consumption. For each building block, we propose a solution to limit the energy consumption without affecting the overall analysis performance. The resulting BRISKOLA (BRISK Optimized for Low-power ARM architectures) feature extractor exhibits energy savings up to 30% with respect to the original implementation.

关键词： Local visual Features BRISK visual sensor networks ARM

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ENCODER RATE CONTROL FOR TRANSFORM DOMAIN WYNER-ZIV VIDEO CODING

ENCODER RATE CONTROL FOR TRANSFORM DOMAIN WYNER-ZIV VIDEO CO...

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10th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Haqqani, M. Raahemifar, K. Fathy, M. Shiraz Univ Dept Comp Shiraz Iran Ryerson Univ Dept Elect Engn Toronto ON Canada Iran Univ Sci & Technol Dept Comp Tehran Iran

ISBN: (纸本)9781479933433

In Distributed Video Coding (DVC) the encoder compresses frames at source rates that rely on the statistical dependency involving the Wyner-Ziv (WZ) and side information frames. A significant issue that individuals address in this paper provides the encoder with a mechanism to spot the origin rate to be found in encoding a WZ frame. One possible solution is to check out a feedback approach;the encoder starts by sending a tiny amount of data (low source rate). Just in case the decoder fails to recuperate the compressed frame, it offers the encoder with a feedback requesting more bits. This solution results in using source rates which can be ideal in the sense that they're the minimal rates that cause successful decoding. Nevertheless, this solution mightn't be practical for visual sensor networks as it can exhaust limited bandwidth and energy resources. To fix these problems, we propose a book encoder rate control (ERC) algorithm for Transform-domain distributed video (TDDV) coder without feedback channel at the price of slightly increased encoder complexity. The experiment results reveal that the performance obtained using our ERC algorithm can be compared to that obtained utilizing the decoder rate control (DRC) algorithm.

关键词： Distributed Video Coding visual sensor networks Encoder Rate Control ERC

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A visual sensor Network for Object Recognition: Testbed Realization

A Visual Sensor Network for Object Recognition: Testbed Real...

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18th International Conference on Digital Signal Processing (DSP)

作者： Redondi, A. Baroffio, L. Canclini, A. Cesana, M. Tagliasacchi, M. Politecn Milan Dipartimento Elettron & Informaz I-20133 Milan Italy

ISBN: (纸本)9781467358057

This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy.

关键词： visual sensor networks object recognition feature extraction

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