Person Re-Identification and Tracking in Multiple Non-Overlapping Cameras - Keynote

Explore person re-identification and tracking across multiple non-overlapping cameras in this 52-minute keynote presentation from the University of Central Florida. Delve into challenges, classification networks, and advanced techniques like SPREID and human semantic parsing. Learn about triplet and quadruplet loss-based networks, diffusion-based approaches, and DCDS networks. Examine the pipeline for person re-identification, including dominant sets clustering, constrained dominant sets, and auxiliary networks. Discover various camera configurations for tracking, including fixed, overlapping, and moving cameras. Gain insights into cross-camera track association, constraint expansion, and track refinement techniques. Conclude with a summary of experimental results for multiple camera setups in this comprehensive overview of cutting-edge person re-identification and tracking methodologies.

Intro
Challenges
Person Classification Network
SPREID - Human Semantic Parsing
Triplet Loss Based Network
Quadruplet Loss Based Network
Diffusion Based Network
DCDS Based Network
Pipeline
Dominant Sets Clustering
Constrained Dominant Sets (CDS)
Auxiliary Net
At Testing
Constraint Expansion
Different Camera Configurations For Tracking
Multiple Fixed & Overlapping Cameras Tracking
Tracking Objects Across Multiple Moving Cameras
Multiple Camera Tracking
Our Approach
Second Layer (Track Generation)
Cross Camera Track Association
Camera-1 as a Constraint
Track Refinement Constraints
Experimental Results Multiple Cameras
Summary