Day #274 - Re_id Notes from papers / Analysis - Reidentification of person from historical data

Approach

• Extract Features
• Cluster to find similar faces
• Approximate k-NN search

Survey on Deep Learning Techniques for Person Re-Identification
Classification Model

• Using SIFT, Color Histograms
• Determining the individual identity (aka class)
• Image Categorization by Age / Gender and Search

Siamese Network 

• Learning a similarity function, which takes two images as input and expresses how similar they are.
• Triplet Siamese model, Pairwise Model
• Triplet models - The triplet loss function takes face encoding of three images anchor, positive and negative.  Here anchor and positive are the images of same person whereas negative is the image of a different person

Face Search at Scale: 80 Million Gallery
Key Points

• Represent objects with feature vectors 
• Employ an indexing or approximate search scheme in the feature space

Performance-oriented Design

• Fast filtering step (Approximate k-NN search)
• Re-ranking step (K Candidates Deep Feature Similarity)

Using Siamese Networks - Retail Use Cases

• Scenario #1 – Find a person in Camera1 and Find him across all other cameras
• Scenario #2 – Find a person at Entrance and Track him across in-store video
• Scenario #3 – Retrain this for every +/- 10 minutes, Dynamically Track for every single customer, Retrain as Class – Query Image Scenario

Happy Learning!!!