2 Minute Paper Summary. Key Planogram Papers - Notes
Planogram Compliance Checking Based on Detection of Recurring Patterns
Product Quantity, Product Size
Paper #2 - Retail Shelf Analytics Through Image Processing and Deep Learning
Shelves Number, Cluster, Shelf Type, Promotional Campaign
Implementation
Planogram Compliance Checking Based on Detection of Recurring Patterns
- Planogram Factors - Regulate product placements
- Conventional Planogram Compliance - Done Manually
- Compliance Checking - Detect Product layout, template matching
- Image Processing - Light conditions, viewpoints of product images, image pattern variations due to seasonal promotions
- Estimated layout vs Estimated product layout
- Spectral graph matching
- Recurring pattern detection
- SIFT / SURF
- Cascade object detection
- Common visual pattern discovery
- Co-recognition / segmentation of objects
- Object matching with features
- Clustering of images
- Visual words and Objects
- Number of rows in shelf
- Number of products in each row
- Type of product vs Shelf Type
- Customer brand positioning
- Competitor band positioning
- Product per customer brand influence
- Products positioned at face height has higher visibility
- Customer presence over shelf type
- Customer presence over promotional campaign
- Customer density over shelf type
- Customer density over promotional campaign
Product Quantity, Product Size
Paper #2 - Retail Shelf Analytics Through Image Processing and Deep Learning
- Coco, Imagenet do not provide annotation to tackle instore product recognition
- HOG based object detection
- Viola - Jones
- SIFT
- HSV color based
- Mask RCNN
- Region based detectors
- Object Detection
- Instance Segmentation
- Key point detection
- Class of product identified
- Bounding Box position
- Number of pixels
- Number of Shelves
- Shelf Type
- Promotional Campaign type
- Cluster type
Shelves Number, Cluster, Shelf Type, Promotional Campaign
Implementation
- Object Detection / Mask RCNN
- Query and find similar images against dataset
- Mask RCNN + VGG for feature extraction + KNN
- brand detector, product detector
- Predictions vs Ground truth items
- IOU
- Per class detection
- Per class segmentation
Paper #3 - Precise Detection in Densely Packed Scenes
Key Notes
- Deep-learning based method for precise object detection
- Estimating the Jaccard index as a detection quality score
- Extensive, annotated data set, SKU-110K
- Soft Intersection over Union (Soft-IoU) network layer
- Represent detections as a Mixture of Gaussians (MoG)
Object detection
- Sliding window-based approaches
- Determine Region Proposals
- Apply Classifiers
- FPN - Feature Pyramid Network
Merge Duplicate Detections
- Non-max suppression
- Agglomerative, affinity propagation clustering
Key Experiments
- Deep IoU detection network
- Soft IOU layer
- EM-Merger unit
- Detection output (x,y,h,w)
- IOU - Intersection / Union (%% of overlap)
EM-approach for selecting detections
E Step assign each box to the nearest box cluster
Box similarity KL distance between corresponding Gaussians
Used for fast clustering
Detection Methods
- Faster-RCNN
- YOLO9000
- RetinaNet
- Infra - Intel(R) Core(TM) i7-5930K CPU@3.50GHz GeForce and a GTX Titan X GPU
Dataset - Link
Presentation - Link
Code - Link. A lot of reusable code is shared in customizing the detections
Poster - Link
Happy Learning!!!
