Sistemi rinconfigurabili a basso consumo per il monitoraggio distribuito
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1 Sistemi rinconfigurabili a basso consumo per il monitoraggio distribuito Michele Magno Tutor: prof. Luca Benini
2 Outline Wireless Video sensor Node Wireless Video sensor Network, WVN Power issue Energy Harvesting PIR Sensor Multomodal low cost system Application of video surveillance Resource manager for embedded computer
3 Wireless Video Sensor Network (WVSN) WSN with cameras for ambient monitoring Unobtrusive No cables (remote locations) Power availability issue Battery powered Sustainable operation Processing Wireless communication Multimodal surveillance networks Augment information Reduce power consumption Energy harvesting techniques to extend node lifetime Extend nodes lifetime
4 Power issue Energy consumption issue Image acquisition and processing Wireless transmission of recorded transmission Several techniques can be used to extend network lifetime Multimodal surveillance Energy harvesting Local processing of images Resource Manger
5 Energy efficient Energy efficient Wireless Video Sensor Node Combines three modalities Multimodal sensing A Pyroelectric InfraRed (PIR) sensor provides a low power wake up signal. Sensor sensibility is modulated according to available energy and image contrast. Energy harvesting A PhotoVoltaic (PV) module is used to harvest energy from the environment. Local computation Images are locally classified using Support Vector Machines (SVM) to detect relevant event and limit wireless transmission of undesired images
6 Wireless Video Sensor Node Samrt Camera =Camera + intelligence The basis for new applications Such as: detection, tracking, scene analysis APPLICATIONS: Automotive Mobile Comm. Surveillance Consumer
7 Wireless Video Sensor Node (II) Low cost Low power consumption Local "intelligence" (on-board image processing capability) A reconfigurable architecture to achieve flexibility Wireless connectivity Increase battery live
8 Wireless Video Sensor Node (III) Processing unit Technology Processor approach: DSP, Media Processor, GPU, ARM Programmable Logic approach: CPLD, FPGA,
9 Application with smart camera People Detection Video Processing Block Current Frame FPGA Background Subtraction Winows Transfer Feature Extraction MICRO Embedded Support Vector Machine(ERSVM) Background
10 Application People detection II ARM9 STR912 Three frame difference: background subtraction Region of Interest (ROI) detection (128x64 pixels) Feature extraction: average gray value over column and row (192 features) Classification: SVM-like hardware oriented algorithm
11 Sensors PIR Sensor conditioning circuits: band pass filter (0.2-12Hz) + amplifier (1500x) + trigger generator Low power: 1.5mW Detects changes in incident IR radiation Moving bodies PIR output dependencies Speed Temperature Distance Lens array Incident radiation + + Sensitive elements Vdd Out Absorbing structure GND
12 Architecture video sensor node + PIR
13 Typical use of PIR Sensor SLEEP MODE VIDEO PROCESSING Wireless Transceiver 2.4GHz
14 Typical scenario
15 Application III abandoned /removed object detection with PIR Sensor NO EVENTS! Sleep mode, only PIR active Active without video sensor EMPTY SCENE PIR->START VIDEO PROCESSING
16 Application IV ZigBee WSN PIR- CAMERA Zibgee comunication Scalable topology Interaction Pir sensors and cameras motes
17 Application IV ZigBee WSN PIR- CAMERA Power consumption measurement Extended battery life Node coopeation
18 Energy Harvesting & MPC ( ETH- ZURICH) Model Predictive Control for rates and other parameters Distributed MPC Control the PIR sensitivity Advance Multimodal video surveillance system
19 Resource Manager for embedded system (SCALOPES EU PRPJECT & STM) Objective: A Linux-based RM infrastructure for the configurable SPEAR platform (multi-core processor + domain specific configurable accelerator) Hw Design & Implementation Thermal sensor Power Islands Clock gating Clock scaling Sw Design Porting of the Linux RM layer on SPEAr platform Adding RM feature to the IPs driver USER LEVEL INTERFACES GOVERNORS ACPI-DRIVER SPEAR ARCH-DRIVER RM-DRIVER APPLICATIONS/ SURVEILLANCE APPLICATION SPECIFIC SPEAR GOVERNOS GENERIC INFRASTRUCTURE CPUIDLE Normal Mode Slow Mode Doze Mode Sleep Mode /sys/devices/system/cpu/cpuidle /sys/devices/system/cpu/cpufreq CPUFREQ Freq. of Cores Freq of DDR Freq of Perif
20 Collaborations PROJECTS EU PROJECT: Scalopes SensactionAAL National PROJECT: SUMMIT PRIIN Industrial STMicroelectronics ( Microcontroller, Vision, SCALOPES) Datalogic ( wireless applications, SUMMIT) Accademic ETH Zurich ( 6 months intership) (MPC & Energy harvesting) EPFL (Andrea Acquaviva - reconfigurable architecture ) University of Trento ( Support Vector Machine) PoliMI ( SCALOPES porjects) PoliTO ( SCALOPES porjects)
21 Publication Journals: Transactions on HiPEAC-1, Lecture Notes in Computer Science (LNCS), Springer- Verlag Berling Heidelberg New York Titolo: Exploration of Reconfiguration Strategies for Environmentally Powered DevicesAutori: Alex E. Susu, Michele Magno, Andrea Acquaviva, David Atienza, Giovanni De Micheli Rivista: Journal of Real-Time Image Processing Titolo: A low-power wireless video sensor node for distributed object detectionautori: Aliaksei Kerhet, Michele Magno, Francesco Leonardi, Andrea Boni and Luca Benini Conferences: Congresso: Advanced Video and Signal based Surveillance Titolo: Distributed Video Surveillance Using Hardware-Friendly Sparse Large Margin Classifiers Autori: Aliaksei Kerhet, Francesco Leonardi, Andrea Boni, Paolo Lombardo, Michele Magno, Luca Benini Luogo: Londra Data: Settembre 2007 Congresso: 11th EUROMICRO Conference on Digital System Design Titolo: A Solarpowered Video Sensor Node for Energy Efficient Multimodal SurveillanceAutori: Magno, Michele; Brunelli, Davide; Zappi, Piero; Benini, LucaLuogo: ParmaData: Settembre 2008 Congresso: Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2) Titolo: Multi-modal Video Surveillance aided by Pyroelectric Infrared Sensors Autori: Michele Magno; Federico Tombari; Davide Brunelli; Luigi Di Stefano; Luca Benini Luogo: Marsiglia Data: Ottobre 2008
22 Publication (II) Congresso: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance Titolo: Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance SystemsAutori: Michele Magno; Federico Tombari; Davide Brunelli; Luigi Di Stefano; Luca BeniniLuogo: GenovaData: Settembre 2009 Congresso: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC 2009) Titolo: Adaptive Power Control for Solar Harvesting Multimodal Wireless Smart Camera Autori: Michele Magno, Davide Brunelli, Lothar Thiele and Luca Benini Luogo: Como Data: Agosto 2009 POSTERS: Congresso: International Conference on Distributed Smart Cameras Titolo: A Low-Power Configurable Wireless Video Sensor Node for Distributed Vision Applications Autori: MAGNO M., L. BENINILuogo: Vienn adata: Settembre 2007 Congresso: Fifth European conference on Wireless Sensor Networks, EWSN 2008 Titolo: A Self-powered Video Node Triggered by PIR Sensors Autori: M. Magno, D. Brunelli, P. Zappi and L. Benini Luogo: Bologna Data: Gennaio 2008 Congresso: 6th European Conference on Wireless Sensor Networks Titolo: Detection of abandoned/removed objects with a video sensor node aided by Infrared Sensor Autori: Michele Magno, Davide Brunelli, Luca Benini Luogo: Cork, Ireland Data: Febbraio 2009
23 Publication (III) Work in progress Congresso: The 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN2010) Titolo Energy Efficient Cooperative Multimodal Ambient Monitoring Autori: Magno M., Zappi P., Brunelli D., L. BENINI STATUS: Submitted TITOLO: Energy aware multimodal video surveillance embedded system. Autori: Magno M., Lanza A. Brunelli D., Di Stefano L., Benini L. TITOLO: Resource manager for video surveillance embedded system. Autori: Magno M., Brunelli D., Benini L. Looking for Transactions of Embedded computer and Embedded video and data signal processing
24 Conclusion and Future Work Energy efficient wireless video node Multimodal surveillance Local preprocessing Multi sensor node for distributed surveillance Video processing application Data fusion for more energy efficiency Energy Harvesting video sensor node Resource manager Future work Augment information from low-power sensors Distributed policies for collaborative surveillance New vision algorithms
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