In this deck, we explain what is video analytics and how does it benefit manufacturing industries.

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.