Video analytics can provide benefits to manufacturing by enhancing revenue, reducing costs, improving quality and capacity utilization, and ensuring compliance. It has applications in process monitoring, intrusion detection, and quality checking. Challenges include handling large video files, network bandwidth limitations, specialized hardware needs, and extracting business value from raw data. Effective strategies involve moving processing to edge devices, only transferring necessary data, designing for repeatability, implementing feedback loops, starting with simple analyses, leveraging video properties like static frames, and exploring self-supervised and semi-supervised learning techniques. Future work may include federated learning and new model architectures for edge devices.

1. Video Analytics
in Manufacturing
Ravikant Bhargava
Co-Founder and R&D Head
Silversparro Technologies

2. What is Video Analytics?

3. Extraction of actionable insights from videos
- Yours truly

4. Caution!!
Buzz words and fancy numbers ahead!
● Industry 4.0
● Manufacturing to go from ~16% of GDP in 2019 to ~25% in
2025
● Industry 4.0 market cap to go from $66.10 Billion in 2017 to
$155.30 Billion in 2024

5. Benefits
1. Enhances top-line
2. OPEX reduction
3. Increased capacity
utilisation
4. Improved quality
5. Adherence to international
standards

6. Possibilities

7. Possibilities
● Process Monitoring - Heavy Industry

9. Possibilities
● Process Monitoring
● Intrusion Detection - Food and Pharma Industry

11. Possibilities
● Process Monitoring
● Intrusion Detection
● Checking on Compliances - Auto Ancillary

13. Possibilities
● Process Monitoring
● Intrusion Detection
● Checking on Compliances
● Quality Checking - FMCG

15. Challenges
● Heavy video files
● Limited network bandwidth
● Need for specialized hardware
● Extracting business value from raw data
● Human stupidity

16. Some Pro Tips!

17. General gyaan
● Move heavy lifting to edge
● Transfer data only when needed and only what is needed
● Design for repeatability
○ Find common patterns
● Plan for ‘fool-proofing’
● Feedback loops
○ Data shift happens
● Pick your battles wisely
○ Have milestones

18. Video data specific gyaan
● Know your data and try simple things first

19. Video data specific gyaan

20. Video data specific gyaan

21. Video data specific gyaan
● Make use of static nature of videos
○ No specialized processing needed
○ Even less data suffices
○ But be wary of rare events
● Not every frame is useful
○ Avoid processing if you can
● Abundance of raw video data

22. Video data specific gyaan
● Abundance of raw video data
○ Where’s my self-supervised learning algorithm
■ Process-of-interest
■ object-of-interest
■ Event-of-interest
○ Semi-supervised/weakly-supervised learning
■ Student-Teacher paradigm

23. Temporal Cycle Consistency

24. Video data specific gyaan
● Supervised learning
○ Use augmentations wisely
■ Not every augmentation is useful
■ But may help create robust model
○ Feedback loops of uncertain data
○ Be innovative in annotating data
■ Use temporal information
■ Mask vs bbox
■ Not every sample is equally useful

25. Some exciting experiments in the pipeline
● Federated learning
○ Saves infra
○ Saves bandwidth
○ Better models
○ Privacy friendly
● Slow learning of temporally coherent matrices (unsupervised)
● Faster, lighter models capable of running in parallel on small
edge devices

26. The best way to predict the
future, is to create it.