Day #149 - Thoughts on Multi Object Classification for Retail Store

We cannot classify all the million objects in Retail Store with a Single Model. We need a mix of different approaches to Detect, extract, Classify and Identify.

• Yolo for bounding boxes and object boundaries
• Model to Detect Humans in Picture
• High-level category classification (Bags, Dresses, Groceries)
• Models for Individual product level Identification (Nike, Puma, American Tourister Bags)

Data preparation depends on Lighting, Environment Factors, We need to use the Surveillance, existing video setup to leverage them for Dataset prpreparationContinually evaluate and re-label false positives. Also, Add Data Augmentation critical to improving on algorithm accuracy.

Next Level Challenges are

• Object Tracking between frames
• Object Occlusion
• Counting and Tracking of Items

The Data Sources / Factors for Billing Items Counting are

• Timeframe of transaction
• Distinct Objects in the timeframe
• Duplicate Objects in a single frame
• Totally we need to have Distinct Object Type and Values, Unique Object Count

Data Issues While Training / Testing

• Class Imbalance
• Projection of camera and angle between training and test images
• Discarding frames with multiple products as (Others)
• Worked on Re-training dataset dozen times to get 80+ accuracy using Random Forest Model
• Ensemble techniques to arrive at multiple predictions and considering voting majority

Improving Model Accuracy

• Ensemble Models
• Voting based classifiers
• Use Adaboost / XGBoost

More Techniques

• Leverage Yolo
• Try Both Contour Detection Techniques
• Try with White background (Contrast Improve)

Setting up a Model for Retail Environment

• Automate Data collection
• Duplicate Yolo with Retail Objects
• First of the kind to come up with Retail Model 
• Keep Objects with a boxed structure / white backgroud
• Generic to customer / POS Checkoout
• Yoflow already tensorflow implementation available
• https://github.com/johnwlambert/YoloTensorFlow229

#LearningContinues