Explore a comprehensive lecture on advanced computer vision techniques for crowd analysis and human detection. Delve into spatial Poisson counting processes, multi-scale Markov Random Fields, and deformable parts models. Learn about innovative approaches to head detection, interest point analysis, and fusion methods. Examine quantitative results, performance analyses, and step-wise improvements in detection algorithms. Investigate the application of these techniques to datasets like UCF-HDD, and understand their effectiveness across various crowd densities. Discover methods for identifying prominent individuals and modeling crowd behavior using neighborhood motion concurrence. Gain insights into hierarchical tracking updates and their significance in visual and contextual information processing for crowd analysis.
Crowd Analysis and Detection Techniques for Computer Vision
University of Central Florida via YouTube
Overview
Syllabus
Motivation
Key Ideas
Problems
Spatial Poisson Counting Process
Patches: Head Detections
Patches: Fourier Analysis
Patches: Interest Points
Patches: Fusion
Images: Multi-scale MRF
Results: Quantitative
Results: Per Patch Analysis • Mean and St. dev per patch for 50 images
Results: Performance Analysis
Results: Analysis of 10th Group
Schematic Outline
Search results: Uniform Grid
Finding Representative Templates
Hypotheses Selection
Optimization
SOS Constraints
Bint Quadratic Programming
Background: Deformable Parts Model
Framework
Scale and Confidence Priors
Intermediate Results
Combination-of-Parts (COP) Detection
Dataset: UCF-HDDC
Results: Qualitative
Results: Step-wise Improvement Contributions of three aspects
Results: Density based Analysis • Evaluation on four different densities: low, medium, high and extreme
Results: Failure Cases
Prominence
Queen Detection
Detection of Prominent Individuals
Modeling Crowd Behavior
Neighborhood Motion Concurrence
Tracking: Hierarchical Update
Experiments: Sequences
Quantitative Comparison
Component Contribution
Chapter Summary · Significance of visual (appearance) and contextual (NMC) information for tracking
Dissertation Conclusion
Future Work
Taught by
UCF CRCV
