Crowd Analysis and Detection Techniques for Computer Vision

Crowd Analysis and Detection Techniques for Computer Vision

UCF CRCV via YouTube Direct link

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39 of 42

Component Contribution

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Crowd Analysis and Detection Techniques for Computer Vision

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  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

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