Explore advanced computer vision techniques for analyzing extremely dense crowded scenes in this doctoral dissertation defense. Delve into novel approaches for counting, detecting, and tracking individuals in images and videos containing hundreds or thousands of people. Learn about methods combining low confidence head detection, texture repetition analysis, and frequency-domain processing. Discover how Markov Random Fields are employed to account for count disparities across scales and neighborhoods. Examine the use of binary integer least squares with Special Ordered Set Type 1 constraints for hypothesis selection. Investigate context-aware human detection in low to medium density crowds using locally-consistent scale priors. Study tracking techniques for dense crowds, including the identification of prominent individuals and the application of Neighborhood Motion Concurrence to model crowd behavior. Gain insights into cutting-edge research addressing challenges in computer vision and crowd analysis through detailed explanations, methodologies, and experimental results presented in this comprehensive oral examination. Read more