Smart camera monitoring system

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Smart camera monitoring system

  1. 1. SMART CAMERA MONITORING SYSTEM ENVIRONMENTAL ANALYSIS, MONITORING AND CONTROL Akshay S Arvind Krishnaa.J Bhargavi R Balamurugan S Divya P Sarang B Third Year, Computer Science and Engineering
  2. 2. Objective <ul><li>To develop </li></ul><ul><li>An effective monitoring system </li></ul><ul><li>Analyze its environment </li></ul><ul><li>Recognize entities </li></ul><ul><li>Track their motion paths and characteristics </li></ul><ul><li>Process above data to obtain identifiable patterns </li></ul><ul><li>Apply or interpret these patterns to suitable applications </li></ul>
  3. 3. Motivations <ul><li>Casino Surveillance System </li></ul><ul><li>Security monitoring and analysis </li></ul><ul><li>Geriatric Surveillance </li></ul><ul><li>Child Care Monitoring </li></ul><ul><li>Industrial Surveillance </li></ul><ul><li>Traffic Management System </li></ul>
  4. 4. System Model- Various Modules ENTITY DETECTION ENTITY RECOGNITION MOTION TRACKING STORING SPATIO-TEMPORAL DATA IN DATABASE MINING DATA TO FORM PATTERNS COMPARE INPUT TO AVAILABLE PATTERNS SELECT QUERY INSERT QUERY ACTION
  5. 5. Overview <ul><li>Real-Time Face Detection </li></ul><ul><li>Facial Recognition and object tagging </li></ul><ul><li>Motion path tracking using SIFT </li></ul><ul><li>Continuously stream processed data from surveillance equipment(cameras) to database residing in the server. </li></ul><ul><li>Form recognizable patterns from the data based on fuzzy logic. </li></ul><ul><li>Use the patterns as a template for future monitoring. </li></ul>
  6. 6. Salient Features <ul><li>Fully automated system with minimal manual monitoring of surveillance footage. </li></ul><ul><li>Ability to scale from very large or medium-small applications </li></ul><ul><li>Integrating existing technologies and building upon the fundamentals. </li></ul><ul><li>Distributed processing of captured data on “smart” cameras instead of on a server </li></ul>
  7. 7. Challenges Faced <ul><li>Detecting individual faces in a densely populated area </li></ul><ul><li>Constructing patterns when subjects are only partially visible </li></ul><ul><li>Monitoring in hostile environments </li></ul><ul><li>Cost and design feasibility </li></ul>
  8. 8. References <ul><li>Viola Paul, Jones Michael, “ Rapid Object Detection Using a Boosted Cascade of Simple Features ” ; ACCEPTED CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2001 </li></ul><ul><li>Henry A. Rowley, Shumeet Baluja, and Takeo Kanade, “ Neural Network-Based Face Detection ” ; PAMI, January 1998 </li></ul><ul><li>“ FPGAs Provide Reconfigurable DSP Solutions ”, White Paper developed by ALTERA International, </li></ul><ul><li>“ FPGA Vs. DSP Design Reliability and Maintenance ” , White Paper developed by ALTERA International. </li></ul><ul><li>Henry Schneiderman, Takeo Kanade, &quot; A Statistical Method for 3D Object Detection Applied to Faces and Cars &quot; cvpr, vol. 1, pp.1746, 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'00) - Volume 1, 2000 </li></ul><ul><li>Jiyong Zhang , “ Spatio-Temporal Databases ”, Spatio-temporal database research at the University of Melbourne </li></ul><ul><li>  Laptev, Ivan and Lindeberg, Tony (2004). ” Local descriptors for spatio-temporal recognition ”. ECCV'04 Workshop on Spatial Coherence for Visual Motion Analysis, Springer Lecture Notes in Computer Science, Volume 3667. pp. 91–103.  </li></ul><ul><li>Zhen Liang, Hong Fu, Zheru Chi, David Dagan Feng , “ Salient-SIFT for Image Retrieval ” ACIVS (1) 2010:pages 62-71 </li></ul>

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