Counting the World with AI Models
•Download as PPTX, PDF•
4 likes•978 views
This document discusses using machine learning and computer vision techniques to count people and objects in images. It provides examples of counting crowds, penguins, and biological cells. Density-based estimations are described as an approach to crowd counting that can handle scale variations and occlusion. Challenges with counting penguins like camera angles and perspective distortion are also outlined. The document emphasizes that counting models need to be integrated into real-world business workflows to be useful.
Recommended
More Related Content
Similar to Counting the World with AI Models
Similar to Counting the World with AI Models (20)
More from Ganes Kesari
More from Ganes Kesari (20)
Recently uploaded
Recently uploaded (20)
Counting the World with AI Models
- 1. Counting the World with AI Models Ganes Kesari, Gramener
- 2. Introduction 2 Co-founder & Head of Analytics Insights as Stories 100+ Clients Help apply & adopt Analytics “Simplify Data Science for all” Our data science platform, Gramex is now open-sourced
- 3. What was the count really?! 3 https://www.theguardian.com/world/2018/sep/06/donald-trump-inauguration-crowd-size-photos-edited#img-1
- 4. Machine Learning 101 4 New Input Desired Outcome Machine learning how to do the job Known Input Known Outcome “Programs that solve the problem” “Programs that learn to solve the problem” vs
- 5. Why Deep Learning? 5 Input Output Identify features to teach model Traditional Machine Learning Deep Learning Person Name Input Output Model automatically identifies features to learn Person Name https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
- 6. Not very different from how we learn Training ..Versatile Detection!
- 8. Approaches to Crowd Counting 8 Occlusion Density Difference Perspective Distortion Camera angle
- 9. Counting using Density-based Estimations 9 Preserve spatial information Localize count Handle scale variations No longer looking for a head! https://blog.dimroc.com/2017/11/19/counting-crowds-and-lines/
- 10. Let’s save the Penguins 10 Penguin populations are dwindling in Antarctica and we must act now Source: Giphy (https://giphy.com/gifs/push-bwLowbhUWm2lO)
- 11. Tracking the Penguins 11 https://www.zooniverse.org/projects/penguintom79/penguin-watch/about/research ~100 cameras in 16 locations, and hourly images over years Crowd-sourced annotations to identify the penguins
- 12. Crowd-sourced Annotations 12 https://www.zooniverse.org/projects/penguintom79/penguin-watch/classify
- 13. Challenges 13 https://www.robots.ox.ac.uk/~vgg/data/penguins/ Cleaned dataset: • Training: 18k • Validation: 3k • Test: 9k Hurdles • Camera angles, • Occlusion, • Perspective distortion, • Density difference, • Weather conditions
- 14. Approach 14 https://arxiv.org/pdf/1707.09605.pdf • High-level prior to classify image into buckets • Density estimation to create the density map Input an image Density Map Estimate the countSplit into 9 patches • NC6 v3 virtual machine with V100 GPU card • Trained for 200 epochs, MAE for the model : ~10.5 Work done by Gramener in partnership with Microsoft AI for Earth
- 15. Walkthrough 15 Work done by Gramener in partnership with Microsoft AI for Earth
- 16. Sizing up human crowds 16 Getting estimates of people can be of use in several use cases such as security, retail and marketing Source: Giphy (https://giphy.com/gifs/nbc-thanksgiving-xUOxeRSgzGd7rbAqWs)
- 18. Counting Biological cells 18 Counting of biological cells is a painful, manual process that takes time of trained pathologists Source: Giphy
- 19. Counting Biological cells 19 Key differences: • Data volume • Labels • Fuzzy entities • Boundaries • Counts aren’t actionable • Business workflow • Low accuracies
- 21. Business workflow for counting 21
- 22. Business workflow for counting 22
- 23. Learnings: Enterprise Application 23 • Label your own data • Look out for practical data challenges • Don’t stop at counts - go for actionability • Build it into the business workflow • Sensitize users on low accuracy • Plan for model refresh
- 24. Best time to buy a burger?! 24 https://blog.dimroc.com/2017/11/19/counting-crowds-and-lines/
- 25. Additional References • Single-Image Crowd Counting via Multi-Column Convolutional Neural Network, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 589-597; Zhang, Yingying Zhou, Desen Chen, Siqin Gao, Shenghua Ma, Yi, • CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting, Vishwanath A. Sindagi, Vishal M. Patel • Counting in the Wild, Carlos Arteta, Victor Lempitsky, Andrew Zisserman, University of Oxford, UK; Skoltech, Russia (https://www.robots.ox.ac.uk/~vgg/data/penguins/) • CrowdNet: A Deep Convolutional Network for Dense Crowd Counting, 2016; Boominathan, Lokesh Kruthiventi, Srinivas S S Babu, R. Venkatesh • Cross-scene crowd counting via deep convolutional neural networks, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, 833-841; Zhang, Cong Li, Hongsheng Wang, Xiaogang Yang, Xiaokang. • Switching convolutional neural network for crowd counting, Sam, Deepak Babu Surya, Shiv Babu, R. Venkatesh, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 4031-4039 25