SRS-NET Smart Resource Aware Multi Sensor Network

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SRS-NET Smart Resource Aware Multi Sensor Network

  1. 1. SMART RESOURCE-AWAREMULTI-SENSOR NETWORKINTERREG IV RESEARCH PROJECTAutonomous complex event detectionin scenarios with limited infrastructure  Klagenfurt, September 2, 2011MASSIMILIANO VALOTTOPAOLO OMEROSABRINA LONDEROvaloo@infofactory.it  -­‐  omero@infofactory.it  -­‐  londero@infofactory.it  hp://www.infofactory.it   1  
  2. 2. MAIN GOAL : SMART MULTISENSOR NETWORKDesigning a smart resource-aware 
MULTISENSOR NETWORK

capable of autonomously DETECTING andLOCALIZING various EVENTS 
such as screams, animal noise, tracks ofPERSONS and more COMPLEX HUMAN %BEHAVIOURS."       2  
  3. 3. RESEARCH AREAS1. NETWORK RECONFIGURATION 2. AUDIO/VIDEO ANALISYS Due  to  limited  resources,  the  sensors   Video  frames  and  audio  signals  are  analyzed   network  should  be  able  to  reconfigure  itself  in   in  order  to  recognize  objects  and  sounds.  We   order  to  limit  consumes  (for  example   can  idenKfy  for  example  the  type,  speed,   switching  off  cameras  when  nothing  happens   direc2on  and  the  coordinates  of  a  moving   in  that  area).   object.  It  is  possible  to  recognize  different   classes  of  objects  such  as  humans,  cars,  dogs   and  cows.  3. COMPLEX EVENT DETECTION Seman2c  analysis  is  performed  over  data   4. MULTIMEDIA DB, RETRIEVAL & ANALYSIS extracted  during  audio  and  video  analysis,   % in  order  to  detect  complex  events,  such  as   The  MulKMedia  DB  is  devoted  to  archive  the   for  example     video  and  audio  files  received  from  sensors.   <people  shoo2ng  to  deers>     Furthermore  the  system  is  consKtuted  by  an   <person  walking  in  a  restricted  area>   advanced  access  &  retrieval  &  knowledge-­‐3   <dog  figh2ng  with  person>   discovery  layer     For  this  purpose  we  use  an  ontological   model  and  a  rules  engine.  
  4. 4. NETWORK SOLAR POWERED AUTO RECONFIGURABLEACQUISITION VIDEO AUDIO PICTURESANALYSIS SOUND DETECTION OBJECT RECOGNITION LOCALIZATIONCOLLECTING SEMANTIC ANALISYS COMPLEX EVENT DETECTED MULTIMEDIA & EVENTS ARCHIVEDATA MINING
  5. 5. 1. NETWORK RECONFIGURATIONOperate the network at highest possible performancewhile minimizing resource usage."   Change  power  mode  of   nodes  and  components         Dynamically  adapt  network  structure     and  node  configura2on  according  to   Find  op2mal  resource   current  applica2on  requirements   alloca2on  in  the  network       LOW  ACTIVITY  à  exchange  only  status     informa2on,  power  down  as  many  sensors  as   Move  cameras  in  order  to   possible   follow  the  scene  of  ac2on     HIGH  ACTIVITY  à  exchange  control  and  data   and  switch  on  a  camera   messages,  ac2vate  as  much  sensors  as   when  something  is  expected   needed   to  happen  in  a  specific  area  
  6. 6. 2. AUDIO & VIDEO ANALYSIS3D Localization, recognition and classification 
of audio sources. " Localiza2on  of    sound   sources  with  2me  difference   of  arrival  (TDOA)     Classifica2on  of  audio   sources.     Iden2fy  specific  sound   paRerns  based  on   characteris2c  features     waves  hit  the  microphones  at   Examples:  barking  dogs,   different  2me  instances  TDOA  is   shou2ng  humans     related  to  the  line  of  origin  of  the   sound  wave    
  7. 7. 2. AUDIO & VIDEO ANALYSISAnalysis and PTZ-Cameras re-configuration. " Detect  simple  paRerns   SOLUTION:     of  ac2vity  on  a  ground   Project  real  world  on   camera-­‐based  reference   map.     system     Cover  the  paRerns  with     The  new  configura2on   conic  sec2ons   op2mally  covers  the  area   represen2ng  the   wrt.  the  ac2vi2es   occurring  in  it.     observed  zone  for  each   video  sensor    
  8. 8. 3. COMPLEX EVENT DETECTIONDetect simple and complex events by means of aconsistent ontology. " Define  simple  and   complex  events  by  means   of  a  consistent  ontology     Describe  the  events’   context,  ie.,  spa2al,   temporal,  object  and   event  rela2onships       Apply  reasoning   mechanisms  to  iden2fy   complex  events  from  low   level  features    
  9. 9. 4. MULTIMEDIA DATA BASE, RETRIEVAL & ANALYSISCollect multimedia data from each sensor, saveevents, and perform advanced analysis." Store  mul2media  data,  low   Find  paRerns  in  data     level  features,  simple  and   Recurring  events  (e,g.  Visitors  are   used  to  stop  in  a  specific  area)   complex  events  in  a   Find  rela2ons  between  events  (event   mul2media  database     “a  deer  is  detected  in  the  morning  in   AREA  1”  is  ocen  followed  by  “the     deer  is  detected  in  AREA  2  in  the   Provide  user  interface  for   acernoon”)à  path  discovery     operators  –  High-­‐level  view   of  “what  is  going  on“       Alert  an  operator   Formulate  complex  queries   Alert  an  operator  using  mobile   (e.g.,all  events  in  a  certain   devices.     area,  the  areas  most   Provide  a  mobile  interface  to  access     the  event  descrip2on  and  the  audio/ frequented  by  bears,  the   video  data   sensors  less  ac2ve,  …)      
  10. 10. AN EXAMPLE OF THE EVENT DETECTION PROCESS A camera recognizes a deer" " A shot is detected by a microphones array in the same area" The position of the hunter is computed" The network is reconfigured to look at the hunter position" The person (hunter) is detected by a camera" The system alerts an operator and sends the event description “a hunter shot a deer” and the audio/video data"
  11. 11. POWER SEARCH.The user interface allows users toperform powerful retrievaloperations over the collected data andadvanced statistical analysis to getknowledge from the archive.The basic access metaphor used forquerying the archive is a what/where/when three dimensional space. 11  
  12. 12. EVENTS.The search results are visualized andcan be navigated following an event/place/network three dimensionalapproach.The events view shows the list of eventsresulted from the search. For eachevent we can see the date, the involvedsubjects, the action and, if defined,the zone where it happened. We canalso see a map showing the exactposition of the event and any relatedmultimedia content (videos, imagesor audio). 12  
  13. 13. DATA MINING.The application offers to the user alsosome advanced statistical analysis,useful to get knowledge from thearchive.Some examples regard the distributionof events of different types over time/inspecific periods or the trend of theactivity of sensors. 13  
  14. 14. MOBILE ACCESS. 14  
  15. 15. PROJECT PARTNERShp://www.uni-­‐klu.ac.at   hp://www.lakeside-­‐labs.com/  hp://www.eye-­‐tech.it/   hp://www.infofactory.it/   15  

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