陸永祥/全球網路攝影機帶來的機會與挑戰

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陸永祥是美國普度大學電機計算機工程學副教授。他是 ACM 傑出的科學家和 ACM 傑出的演講者。他的研究小組用雲計算系統分析網路攝影機的數據。這是開放式系統;目前已經有兩百多個用戶。他從斯坦福大學獲得博士學位。

Published in: Data & Analytics

陸永祥/全球網路攝影機帶來的機會與挑戰

  1. 1. Opportunities and Challenges in Global Network Cameras 全球網路攝影機帶來的機會與挑戰 Yung-Hsiang Lu 陸永祥 Purdue University Acknowledgments: National Science Foundation ACI-1535108, IIP-1530914, OISE-1427808, and CNS-0958487, Lynn CSE Fellowship, Amazon, Microsoft, and the owners of the data. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the sponsors. 1
  2. 2. Purpose of Today's Seminar Share our recent progress Discuss new ideas Recruit users and collaborators Please feel free to interrupt and share your comments / questions / suggestions. 2
  3. 3. 3 臺北市交通控制中心
  4. 4. 4 2016-07-04 08:58:25
  5. 5. 5 高雄市政府交通局 07/04/2016 10:27:25
  6. 6. 6 交通部臺灣區國道高速公路局即時路況資訊 10:58:33 07/04/2016
  7. 7. Demonstration: Image-Based Navigation Purdue University 7
  8. 8. Demonstration: 即時圖像導航 真理大學, 資訊工程學系 蘇維宗 教授 8
  9. 9. 9 Image capture: Mar 2009
  10. 10. Why is real-time data important? 為什麼即時的數據很重要? 10
  11. 11. Emergency Responses 災難救助 11
  12. 12. Houston Flooding 2016/04/18 12 04/18/2016 14:40:03
  13. 13. Parade Route Traffic Cameras 13 公共安全 2014 Thanksgiving Parade in NYC [International Conference on Cloud Computing and Big Data 2015]
  14. 14. Parade Scenes 14
  15. 15. Object Tracking Network cameras provide abundant real-world data for vision programs. 15
  16. 16. 16
  17. 17. One Image = One Thousands Words 17
  18. 18. One Image = One Thousands Words 18 resting groups talking alone female choosing
  19. 19. What do you see? 19
  20. 20. What do you see? 20 standing child nobody resting
  21. 21. CAM2: Continuous Analysis of Many CAMeras http://cam2.ecn.purdue.edu 21
  22. 22. CAM2: Continuous Analysis of Many CAMeras http://cam2.ecn.purdue.edu 22 CAM2: general-purpose computing platform for analyzing large amounts of data.
  23. 23. 23 Temporal Spatial real-time recent obsolete single few many worldwide stationary network camera personal photographs organizations' network cameras CAM2 photographs on the Internet street view
  24. 24. Who Can Use CAM2? 誰可以使用CAM2 ? Big Data 大數據 Computer Vision 計算機視覺 Cloud Computing 雲計算 Mobile Computing 移動計算 Programming Language 程序設計語言 Architecture 計算機結構 Network 網路 Human Interface 人機界面 You 你! 24
  25. 25. CAM2 has demonstrated the ability to • analyze 200 million images (7TB) in 24 hours • (200 M images = 1 image/sec for 8.8 years) • from 16,000 cameras worldwide • one live (real-time) image every 5 seconds • 17 Amazon high-performance instances • detect motion (background subtraction) working on analyzing 1B images/day now 25
  26. 26. Background Subtraction 26
  27. 27. Moving Object and Human Detection 27
  28. 28. Demonstration Object Tracking + Speed 28
  29. 29. 29 user Web Portal user database camera database data sources cloud resource manager CAM2 visual data Visual data do not go through CAM2.
  30. 30. CAM2 has more than 80,000 cameras now, 5 seconds/camera  14 days (8 hours / day) 30 100,000 17
  31. 31. Big Data? • 100,000 cameras • one image/minute-camera  140M images/day • one image/second-camera  8B images/day • Each image ~ 100KB  14 TB ~ 800 TB/day 31
  32. 32. Examples of Dataset 32
  33. 33. Network Cameras for Public Safety 33 ChicagoPurdue [IEEE Technologies for Homeland Security 2016]
  34. 34. Use Network Cameras for Public Safety 34
  35. 35. Privacy Protection We do not identify any individual. Users agree not to identify any individual. 35
  36. 36. 36
  37. 37. 37
  38. 38. 38 CCTV: close circuit television not connected to the Internet
  39. 39. Data for Machine Learning 39
  40. 40. [Cloud Computing and Big Data 2015] [Cloud Computing Technology and Science (Cloudcom) 2015] 40 cameras + locations + resolutions desired frame rate visual content analysis program cloud instances: • types (# cores, memory) • locations • numbers Resource Management
  41. 41. Cloud Computing Locations 41 Amazon EC2's Locations Microsoft Azure's Locations
  42. 42. Cloud Pricing ($/hour) AWS m3.2xlarge (8 vCPU + 30GB memory) 42 0.532 0.585 0.632 0.784 0.616 0.761 0.77 784 532 = 1.474
  43. 43. Bring alldata to the cheapest instance? 43 0.532 0.585 0.632 0.784 0.616 0.761 0.77
  44. 44. Round-Trip Time (RTT) and Frame Rates [IEEE Cloud Computing Magazine September/October 2015] 44 0 5 10 15 20 25 30 35 0 50 100 150 200 250 300 FramesperSecond(fps) Round-Trip Time (RTT) in ms MJPEG measured MJPEG using netem to inject delays If high frame rates are required, data must be retrieved by a cloud instance with small RTT
  45. 45. Nonlinear Frame Rate and Utilization (Amazon m3.xlarge) IA: Image Archival ME: Motion Estimation MOD: Moving Object Detection HD: Human Detection 45 [Cloud Computing and Big Data 2015 (Best Paper Award)] (a) 0.2 frame/s (b) 10 frame/s 0.02% 0.31% 0.20% 0.03% 0.15% 0.21% 0.40% 2.65% 0.1% 0.4% 0.32% 5.78% 8.34% 14.48%
  46. 46. [Transactions on Cloud Computing (submitted for review)] 46 cameras + locations + resolutions desired frame rate visual content analysis program cloud instances: • types (# cores, memory) • locations • numbers Variable-Size Bin Packing stream 1 stream 5 stream 2 stream 7 stream 3 stream 8 stream 4 stream 6 VM1 VM2 VM3
  47. 47. Cost Per Million Frames 47 (a) 0.2 frame/s (b) 10 frame/s [Cloud Computing and Big Data 2015] Choosing the right cloud instance can reduce cost by more than 50% Larger differences at higher frame rates
  48. 48. Resource Management 48 Scenario Program Frame Rate Cameras Intensive Scenario 1 (CPU Intensive) FT 15.00 25 CPU HD 0.50 250 CPU Scenario 2 (Memory Intensive) BS 0.10 5000 Memory MOD 0.05 3000 Memory Scenario 3 (Mixed) BS 0.20 4000 Memory MOD 0.20 1000 CPU FT 10.00 10 CPU HD 0.20 300 CPU FT: Feature Tracking (optical flow) HD: Human Detection (HOG) BS: Background Subtraction MOD: BS + erosion + dilation + contour Abbr. Resource Allocation Strategy ST1 Always use m4.xlarge ST2 Always use c4.xlarge ST3 Always use r3.xlarge ST4 Use the most cost-effective instance for each program without sharing instances between programs ST5 Enhanced Manager: Reduce the overall cost with sharing instances between programs Model and solve the problem using multi-dimensional bin packing The experiments ran for 24 hours as many as 120 cores in AWS.
  49. 49. Evaluation of Resource Allocation 49 61% Savings $326/Day $128/Day 25% Savings37% Savings $336/Day $211/Day CPU Intensive Memory Intensive Mixed $248/Day $185/Day
  50. 50. Analysis for 24 Hours 50 Lectures End
  51. 51. Analyze Archive using Spot Instances • Three types of pricing models: • Spot instances' costs depend on the market. • A spot instance may be terminated when the market price exceed the bidding price. 51 Pricing Model Pay Analogue On-Demand Hourly Hotel Room Long-Term Yearly Apartment Lease Spot Bidding Priceline.com
  52. 52. Offline Analysis of Archival Data • Spot instances can be a cost-effective solution for analyzing archival data (i.e., not real-time). • Using periodic check-pointing, analyses may resume after terminations. • Setting bidding prices strategically can reduce cost (as much as 85%) with less than 5% performance degradation. [Electronic Imaging 2016] 52
  53. 53. Computation Offloading Integrating Mobile and Cloud 53
  54. 54. Lessons Learned (many) • Data management must be planned in advance • Treat the data as "non-persistent": only one chance to touch the data • Metadata must be generated in advance or during data acquisition, not after • When in doubt, save the data and (more important) metadata • Metadata must be machine readable • Encode (some) metadata in file names • Supervised learning (with truth) is impossible 54
  55. 55. Future of CAM2 • Computing platform for analyzing "big data" (TB/h), real-time or archival • Integration of many different sources of data (weather, earthquake, tweets, traffic ...) • Repository of "real-world" visual data • Test bed for system research • Opportunities for collaboration 55
  56. 56. Many Challenges 56 • Create metadata for searching the sources • Develop standards to retrieve data • Find locations of the cameras • Design vision solutions to understand the world • Allocate resources to analyze and store data • .... many more
  57. 57. 57 Acknowledgments
  58. 58. Former Members Started Perceive Inc. and receives $225,000 from NSF SBIR IIP- 1622082 (已經募集七百萬新台幣 ) 58 Yung-Hsiang Lu is a co-founder and the Scientific Adviser of Perceive Inc.
  59. 59. Conclusion • Network cameras provide many opportunities for understand this world. • CAM2 is a system for large-scale analysis. • It is a platform for vision program at large scales as well as cloud resource management. • Please register as users cam2.ecn.purdue.edu. • Source code is available upon request. 59
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