Crowd Analysis and Detection Techniques for Computer Vision

Crowd Analysis and Detection Techniques for Computer Vision

UCF CRCV via YouTube Direct link

Results: Qualitative

27 of 42

27 of 42

Results: Qualitative

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Crowd Analysis and Detection Techniques for Computer Vision

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Motivation
  2. 2 Key Ideas
  3. 3 Problems
  4. 4 Spatial Poisson Counting Process
  5. 5 Patches: Head Detections
  6. 6 Patches: Fourier Analysis
  7. 7 Patches: Interest Points
  8. 8 Patches: Fusion
  9. 9 Images: Multi-scale MRF
  10. 10 Results: Quantitative
  11. 11 Results: Per Patch Analysis • Mean and St. dev per patch for 50 images
  12. 12 Results: Performance Analysis
  13. 13 Results: Analysis of 10th Group
  14. 14 Schematic Outline
  15. 15 Search results: Uniform Grid
  16. 16 Finding Representative Templates
  17. 17 Hypotheses Selection
  18. 18 Optimization
  19. 19 SOS Constraints
  20. 20 Bint Quadratic Programming
  21. 21 Background: Deformable Parts Model
  22. 22 Framework
  23. 23 Scale and Confidence Priors
  24. 24 Intermediate Results
  25. 25 Combination-of-Parts (COP) Detection
  26. 26 Dataset: UCF-HDDC
  27. 27 Results: Qualitative
  28. 28 Results: Step-wise Improvement Contributions of three aspects
  29. 29 Results: Density based Analysis • Evaluation on four different densities: low, medium, high and extreme
  30. 30 Results: Failure Cases
  31. 31 Prominence
  32. 32 Queen Detection
  33. 33 Detection of Prominent Individuals
  34. 34 Modeling Crowd Behavior
  35. 35 Neighborhood Motion Concurrence
  36. 36 Tracking: Hierarchical Update
  37. 37 Experiments: Sequences
  38. 38 Quantitative Comparison
  39. 39 Component Contribution
  40. 40 Chapter Summary · Significance of visual (appearance) and contextual (NMC) information for tracking
  41. 41 Dissertation Conclusion
  42. 42 Future Work

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.