This document summarizes recent progress and opportunities in analyzing data from global network cameras. It discusses the CAM2 system, a general-purpose computing platform for analyzing large amounts of image data from thousands of cameras worldwide. CAM2 has demonstrated the ability to analyze billions of images per day using cloud computing resources. It aims to provide abundant real-world image data and computing power for computer vision and machine learning applications. The document also outlines several challenges in managing and analyzing data from networked cameras at a large scale.
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
陸永祥/全球網路攝影機帶來的機會與挑戰
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. 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
22. CAM2: Continuous Analysis of Many CAMeras
http://cam2.ecn.purdue.edu
22
CAM2: general-purpose
computing platform for analyzing
large amounts of data.
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. 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
43. Bring alldata to the cheapest instance?
43
0.532 0.585
0.632
0.784
0.616
0.761
0.77
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. 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%
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. 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.
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. 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
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. 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. 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
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. 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