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