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TensorFlow London 18: Dr Daniel Martinho-Corbishley, From science to startups with Tensorflow, Computer Vision and people.


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Abstract: Convolutional Neural Networks are the most popular approach to performing image recognition. But how can we move them from the lab to the real world? In this talk Daniel will discuss the challenges of classifying pedestrian demographics in unconstrained environments and using the latest advances in computer vision to solve critical business problems. You can expect to hear about novel image labelling techniques, why people are so valuable, and the future of computer vision.

Bio: Daniel has just completed his PhD in Computer Science and Biometric Identification from the University of Southampton and is now the co-founder and CEO of Aura Vision Labs, a video AI platform specialising in measuring and improving retail shopping experiences. His research involves robust estimation of pedestrian demographics from CCTV imagery using the latest techniques in computer vision and psychological crowdsourcing. Daniel’s research is published in the leading applied machine learning journal, IEEE TPAMI and Aura Vision was featured on BBC Click in May 2018.

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TensorFlow London 18: Dr Daniel Martinho-Corbishley, From science to startups with Tensorflow, Computer Vision and people.

  1. 1. From Science to Startups Dr. Daniel Martinho-Corbishley with Computer Vision,
 Tensorflow & People
  2. 2. Video AI platform for retailers to measure and improve every shopping experience Daniel Jaime Jonathon PhD in Computer Vision and Soft Biometrics Recently published “Super-Fine Attributes with Crowd Prototyping” in IEEE TPAMI Dr. Daniel Martinho-Corbishley
  3. 3. Boston Marathon Bombing (2013)
  4. 4. Facial Recognition - Over 3 days to identify suspects - Extremely hard to spot faces in crowds - Internet surveillance traffic growing 7x in the next 3 years
  5. 5. Stage 1. How to find people
 without seeing their faces?
  6. 6. Gender [Male] Age [25-35] Headwear [Navy Cap] Accessories [Black Rucksack] Upper body [Navy coat, White T-shirt] Lower body [Beige Trousers] Footwear [Brown Shoes] Height [Average - Tall] Weight [Average - Slender] Build [Slightly Muscular] Hair Colour [Black] Skin Colour [Brown] Hair Length [Short] Soft Biometrics - New branch of identity science - Visual cues to identify people - Visible at a distance - Invariant to angel and pose DNA [✘] Iris [✘] Fingerprint [✘] Face [✘] SoftHard - Highly discriminative - Difficult to capture
  7. 7. Which Gender? Female Male
  8. 8. Female Male Which Gender?
  9. 9. Female Male Which Gender?
  10. 10. Stage 2. How to precisely label
 any image?
  11. 11. - Label images as coordinates in super-fine space. - Precisely describes multiple, integral concepts. Male Female Gender Uncertainty ClearObscured X X XX X Super-Fine Attributes - Don’t account for ambiguity or uncertainty. - Irrelevant and inconsistent labels. - Poorly generalised classifiers. Female Female Male Male? ??? Categorical / Binary space Super-fine space
  12. 12. - Crowdsource pairwise similarities between n = 95 subjects. - Forms a high-dimensional distance matrix: - Embed with Metric MDS to discover fewer, more salient concepts:
 - Cluster with Agglomerative Hierarchical clustering to discover c = 5 prototypes , Embedding Clustering Prototype cluster Crowd Prototyping 0 n ⋱ ⋱ ⋱ n ⋱ Distance matrix
  13. 13. Male (0.00, 0.32) Pos. Female (0.64, 0.00) Female (1.00, 0.29) Obscured (0.69, 1.00) Pos. Male (0.30, 0.63) Crowd’s perceptual space & Visual prototypes. Matching new images to visual prototypes. Efficient, large scale super-fine attributes
  14. 14. Very young Quite young Quite Old Very Old Obscured / Can’t See Super-Fine Age Labels PETA dataset - Large-scale - 19000 image samples - Most diverse - 8799 unique subjects - 108 binary attributes
  15. 15. Super-fine +4.02% AUCSuper-fine +8.25% AUC Ranked Retrieval Super-Fine vs Conventional ResNet-152 Gender & Age [Super-fine & Binary] 3 Attributes [Super-fine] 35 Attributes [Binary] CNN Training Binary classified Super-fine regressed
  16. 16. Dataset loading
  17. 17. Image Augmentation Pipeline
  18. 18. Stage 3.
 From the lab to the real-world
  19. 19. Loading Multiple Checkpoints
  20. 20. Stage 4. Commercialisation for retail.
  21. 21. Deep, anonymous insights People counting is a competitive landscape 100% Anonymous Fully GDPR compliant No personal data stored. Shoppers are never identified. Footfall counts Heat maps Product footfall Peel-off rates Product engagement Dwell maps Gender Age
  22. 22. The Product 1 Your campaign drew 5% more females aged 16-24 “ 2 3 Minimal Installation Rapidly integrates with existing CCTV cameras. Capture unique insights Intuitive dashboard and API
 reports shopper insights Define impact Retailers can now measure their performance and ROI. ”
  23. 23. Many thanks! Any Questions? Dr. Daniel Martinho-Corbishley