OSSERVATORIO SMART CITIES ANFOV 6 GIUGNO 2012 PALAZZO GIURECONSULTI VIA MERCANTI 2 MILANO

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1 Rita Cucchiara, SOFTECH-ICT Università di Modena e Reggio Emilia OSSERVATORIO SMART CITIES ANFOV 6 GIUGNO 2012 PALAZZO GIURECONSULTI VIA MERCANTI 2 MILANO

2 Università di Modena e Reggio Emilia Facoltà di Ingegneria Enzo Ferrari Dipartimento di Ingegneria dell Informazione Lab ImageLab, SoftLab.. Facoltà economia Marco Biagi Rete ad Alta tecnologia dell Emila Romagna

3 Projects European THIS Transport hubs intelligent surveillance EU JLS/CHIPS Project VIDI-Video: STREP VI FP EU (VISOR VideosSurveillance Online Repository) International Italian & Regional With enterprise.. BE SAFE NATO Science for Peace project Detection of infiltrated objects for security Australian Council Bheave_Lib : Regione Emilia Romagna Softech LAICA Regione Emilia Romagna; FREE_SURF MIUR PRIN Project VISERAS con Lepida, Ibm Vitrociset CSP 2011 EssE VS PaaS (Distretti tecnologici) con Acantho Building site surveillance: with Bridge-129 Italia Stopped Vehicles with Digitek Srl SmokeWave: with Bridge-129 Italia Sakbot for Traffic Analysis with Traficon Mobile surveillance with Sistemi Integrati 2007 Domotica per disabili: posture detection FCRM

4 Videosurveillance for Smart cities Videosurveilanace in Cloud Distretti Tecnologici in ER lab:esse

5 (Video) Surveillance concerns models, techniques, systems and applications for acquiring data about the 3D external world, detecting targets along the time and the space, recognizing interesting or dangerous situations, generating real-time alarms, recording meaningful data about the controlled scene.

6 Smart economy Smart mobility Video surveillance solutions Smart Governance Smart city Model* Smart Living Smart People Smart Environment *http://www.smart-cities.eu/index2.html

7 Security Safety Statistics Public places Private sites Terrorism Vandalism Enforcement Privacy Analytics for Forensics Personal safety At home/at work Disabled Elders In car safety attentional control Public safety crown monitoring metro/train stations Biometry Monitoring working conditions Counting vehicles people actions Retail, sport events Testing market strategies E-marketing

8 Research Business Hardware research Analogue Cameras systems 80 Digital CCTV systems 90 Digital Surveillance Systems 00 Network Surveillance Systems 80 Military research Signal & Image processing 90 CVPR-based Traffic surveillance 00 PRML based People surveillance 10 Social, ubiquitous surveillance 10 Mobile+ IOT Surveillance and Video Analytics In the Cloud 69 Municipality Building NY 85 the first DVR In surveillace 93 World Trade Center NY Digital VS 05 Chicago Virtual shield 11 Chongqing: the biggest IoT VS system, cameras; tag 200 million cars with RFID

9 1)Electronics companies A/D Cameras, IP Cameras, Smart cameras, Embedded Systems 2)TLC companies Infrastructure, wired/wireless video communications, coding formats 3)IT companies Tools e Software per video analytics, computer vision, pattern recognition, machine learning

10 Before 2020 the 51% of camera market will be IP-based (F&S2009)

11 INTEROPERABILITY STANDARDS (jan 2012) Open Network Video Interface Forum (ONVIF) 295 members, Axis, Bosch, Sony, and Canon, Cisco, Pelco, Samsung, Panasonic, Texas Instruments, Siemens, Honeywell, Lg Electronics; >1000 products Pysical Security Interoperability Alliance (PSIA) 89 members, IBM, Cisco, Tyco, Texas Instruments, Object Video, Pelco, Milestone, Honeywell, Arecont Vision; >200 products CODING STANDARDS M-JPEG vs H264 Streaming, remote services The CLOUD

12 Analogue cameras An.switch Monitors &VCRs Analogue cameras An.switch Monitors &DVR Analogue cameras Pc.based Capture cards Monitors &VCRs Internet gateway digital cameras Analogue cameras switch Monitors &VCRs Digital and IP cameras Internet gateway PC, Server For video analytics

13 Analogue cameras switch Monitors &VCRs Digital and IP cameras Internet gateway PC, Server For video analytics Analogue cameras Digital and IP cameras switch Monitors &VCRs Monitors &VCRs Monitors &VCRs switch Analogue cameras Digital and IP cameras PC, Server For video analytics Analogue cameras switch Analogue cameras switch PC, Server For video analytics Digital and IP cameras Digital and IP cameras

14 fixed cameras Cluster of cameras PTZ cameras Application aas Video analytic tools Mobile cameras Large control centers Platform aas Infrastructure aas Storage And CPUs Surveillance platforms Small control points

15 1. From Architecture to Infrastructure as Services from real-time constraints and GP architecture to 0-time constraints clusters and SP architectures EssE 2. From Middleware to Platforms as Services Research on coding and transcoding Acquisition and synchronization VS in the cloud EssE Visualization 3. From Algorithms to Applications as Services Not all already solved, but a lot of approximately working results! Form motion analysis to moving object analysis Analysis for inferring, recognizing, preventing. Analysis for searching, tagging, surfing. (CV+PR+MM)

16 Acquisition Single camera Multiple cameras Detection Single object/people Multiple targets, cluttered, occluded.. Tracking Linear prediction Generative models Stocastic prediction Understanding Purposive

17 Acquisition Detection & Tracking SPATIO-TEMPORAL REASONING Learning & Inferring BIG DATA ANALYSIS Searching & Recognizing HUMAN FEEDBACK

18 1) people detecting, and searching people detection in large video sets people tracking for short term re-identification people re-identification with 3D models people soft-biometry by searching with relevance-feedback 2) people analysis in crowd crowd detecting] people analysis in crowd people trajectory analysis for social behavior Behavior learning in Surveilled Areas with Feature Extraction BE SAFE

19 People Segmentation* People detection** *S. Calderara, R. Cucchiara, A. Prati,"Bayesian-competitive Consistent Labeling for People Surveillance in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, n. 2, pp , 2008 **G.Gualdi, A.Prati, R.Cucchiara Multi-Stage Particle Windows for Fast and Accurate Object Detection Transactions on Pattern Analysis and Machine Intelligence 2012

20

21 Automatic detection of crowd, group of people, people, faces, identification, re-identification 21

22 Movements, activities, action,behavior analysis

23 Scientific research Academic competitions

24 Services of Image search and Retrieval Different Relevance feedback Methods Spidy

25 25

26 EsseE services for Enterprise

27 New generations of Platforms for managing VS data Acquisition, encoding-decoding Storing capabality CPU use Integration of Platform and Acquisition

28 Goals PaaS model definition Infrastructure settings Benchmarking Designing Testing

29

30 Axis Camera Station (ACS) Axis Video Hosted Service (AVHS) Avigilon Control Center Cisco Digifort DVTel Latitude Genetec Omnicast ipconfigure Exacq Luxriot Milestone Corporate Milestone Enterprise Mobotix NUUO OnSSI NetDVMS & Ocularis Client Lite Pelco Digital Sentry ZoneMinder? Alarm Price Simplicity 3rd Party IP Camera Support 3rd Party Security System Suppo Enterprise Management Analytics Redundancy Video Distribution / Event Management

31 1. Comparison 3 Video Management Systems (VMS) Milestone Xprotect (Global Leader) Genetec Omnicast (Global Leader) ZoneMinder (Open Source) 2. Load analysis of VMS son DataCenter 3. Analysis of functionality for system administrator usability for final user

32 Video Originale Video Benchmarks CPU Load - Encoding COMPRESSIO RISOLUZION FPS SEC NUM NE E FRAMES Video RGB 800x Originale Video A RGB 800x600 12, Video B RGB 400x Codec - Conf Encode Video C Encode MPEG4 Encode IO 800x600 Wr Output Video 25 Occupazione 60 Criticità 1500 Fps Time (MB/s) Bitrate Mbps CPU Core MJPEG - Q ,09 6,81 30,93 50% I/O (RD) MJPEG - Q ,91 1,82 7,73 50% I/O (RD) MJPEG Q ,00 1,23 4,93 50% I/O (RD) MJPEG Q ,85 1,04 4,00 50% I/O (RD) MJPEG - 1Mbps 51 29,41 3,60 14,13 50% I/O (RD) H264 - Q ,00 2,53 50,67 100% CPU H264 - Q10 12,5 120,00 1,49 23,87 100% CPU H264 - Q ,33 0,62 6,93 100% CPU H264 - Q ,46 0,09 0,45 100% CPU H Kbps 32 46,88 0,17 1,07 100% CPU V.A V.B H Kbps 32 46,88 0,17 1,07 100% CPU MJPEG - Q ,71 1,97 3,87 80% I/O (RD) H264 Q10 12,5 60 1,55 12,4 100% CPU MJPEG - Q ,89 2,53 2,67 80% I/O (RD)

33 Video Originale Video Benchmarks CPU Load Transcoding COMPRESSION RISOLUZIONE FPS SEC NUM FRAMES E Video RGB 800x Originale Video A RGB 800x600 12, Video B RGB 400x Video C MPEG4 800x Codec - Conf Encode Fps Encode Time Encode IO Wr (MB/s) Output Video Bitrate Mbps Occupazion e CPU Core Criticit à V.C MJPEG - Q ,27 2,91 7,87 100% CPU H264 - Q10 12,25 122,45 1,43 22,93 100% CPU CPU Load Decoding Codec - Conf Decode Fps Decode Time Decode IO Read MB/s Input Video Bitrate Mbps Occupazion e CPU Core MJPEG - Q ,00 3,87 30,93 5% MJPEG - Q ,00 0,97 7,73 4% MJPEG Q ,00 0,62 4,93 3% MJPEG Q ,00 0,50 4,00 <1% MJPG - 1Mbps 25 60,00 1,77 14,13 3% Criticit à H264 - Q ,00 6,33 50,67 100% CPU H264 - Q ,00 2,98 23,87 50% H264 Q ,00 0,87 6,93 14% H264 Q ,00 0,06 0,45 4% H Kbps 25 60,00 0,13 1,07 3% H Kbps 25 60,00 0,13 1,07 3% V.A H264 - Q10 12,5 60,00 1,55 12,40 9% V.B H264 - Q ,00 0,72 5,73 4%

34 Video Originale Codec - Conf Output Video Bitrate Mbps GB/hour GB/day TB/m MJPEG - Q1 30,93 13,9 334,1 10,0 MJPEG - Q10 7,73 3,5 83,5 2,5 MJPEG - Q20 4,93 2,2 53,3 1,6 MJPEG - Q30 4,00 1,8 43,2 1,3 MJPEG - 1Mbps 14,13 6,4 152,6 4,6 H264 - Q1 50,67 22,8 547,2 16,4 H264 - Q10 23,87 10,7 257,8 7,7 H264 - Q20 6,93 3,1 74,9 2,2 H264 - Q30 0,45 0,2 4,9 0,1 H Kbps 1,07 0,5 11,5 0,3 H Kbps 1,07 0,5 11,5 0,3 V.A MJPEG - Q10 3,87 1,7 41,8 1,3 H264 Q10 12,4 5,6 133,9 4,0 V.B MJPEG - Q10 2,67 2,6 61,9 1,9 H264 - Q10 5,73 1,2 28,8 0,9

35 Comune di Fiorano Modenese 16 video streams Cameras PAL vs. MegaPixel Fixed vs. PTZ Streaming video Given 50Mbps codec H264, MPEG4, MJPEG VMS comparison on same hardware Client requests on LAN

36 Load analysis on datacenter for video recording motion detection streaming video on LAN video transcoding symultaneous queries Live streams from more cameras più flussi live streams from single camera Off-line streams off-line in fast rewind Motion events analysis

37 G=Genetec M=Milestone Test CPU load Disk load Network load inbound Network load outbound Video Recording G: 50% M: 10% 6MB/s 16 cameras generating 50Mbps / Recording with Motion Detection 1 client over LAN browsing 4 live streams (4Mbps each) 4 clients over LAN browsing 1 live stream (4Mbps) G: 50% M: 50% Depending on motion Low / Low / 16 cameras generating 50Mbps G: Null (multi-cast and server by-pass) / G: Null (multi-cast and server by-pass) M: 16Mbps M: 16Mbps G: Null (multi-cast and server by-pass) G: Null (multi-cast and server by-pass) M: 4Mbps M: 16Mbps 1 client over LAN browsing 4 off-line streams (4Mbps each) Low 2MB/s / 16Mbps 1 client over LAN browsing 4 off-line streams (4Mbps each) with fast rewind Low 20MB/s 4MB/s / G: 4Mbps M: 22Mbps

38 User-oriented: user set-up for Video Paas services Time switching Permission restrictions Camera-oriented PTZ and Patrolling Auto discovery Edge functionality (on-board processing) Feature-oriented Privacy, encryption and watermarking Third party plug-ins Access Control Video Analytics

39 Test with non expert users with VMS Installation GUI-oriented: Feature-Oriented:

40 Scalability QoS Privacy Automatic Knowledge extraction Lab EssE Video PaaS

41 thanks

42 New generations of surveillance solutions: -IMG-S Integrated Mission Group for Security (Ta1-Ta3 Surveillance and identification group) The mission is to provide recommendations for R&D at European Level (16 countries, 86 members) Call : physical and logical security, data analysis, forensics, crowd, sensors analysis, interfaces.. from system to services Bruxelles, 21 may 2012

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