A Video Analytics Solution for Discovering Customer Shopping Behavior in Retail Stores

Transcription

1 A Video Analytics Solution for Discovering Customer Shopping Behavior in Retail Stores Authors: Ayushmaan Basu; Harikrishna G. N. Rai; Shreya Barsaiyan; K Sai Deepak; P. Radha Krishna; Lokendra Shastri Speaker: Harikrishna Rai Research Analyst Infosys Labs, Bangalore 2012 The MathWorks, Inc. 1

2 Presentation Overview Why analytics in retail stores? Requirements & Assumptions Challenges in Computer vision Solution overview SCID design overview Algorithm overview Matlab benefits & toolkits used Solution benefits 2

3 Why Analytics in Retail Stores? Retailers need personalized and location based services to improve their customers shopping experience Retailers & CPG companies need depth knowledge of customers data Shopper Behavior, Buying Patterns and Shelf Activity Enable Strategic Marketing (product placement) Time spent on products Shopper path In-store analytics Floor map analysis Stop points Customer profiling is key to offer any such customer engagement solutions RFID & WSN solutions In-store analytics help stores to take better decisions about how to tailor sales, coupons and other promotional campaigns Get insights into how consumers react to various displays and digital merchandising scenarios Expensive as separate hardware need to attached to each cart Maintenance is costly Chances of damage to these hardware devices Poor scalability 3

4 Solution Requirements & Assumptions Requirements Solution to be simple to use and cost effective Zero maintainace Robust and highly accurate Scalable solution without change in store setup Provide additional value added services Light weight solution with minimal processor requirement Assumptions Typical retail store setup with aisles for shopping Wall/rack mounted camera Camera fixed at height of 8-10 feet Multiple presence of carts Customer movement in moderate speed Reasonable lighting conditions 4

5 Challenges for computer vision approach Low resolution images Varied Lighting condition Shadows Occlusion Orientation Scale Perspective projection 5

6 Demo Single Cart Scenario Demo Multiple Cart Scenario 6

7 video stream Solution Overview camera distance field of view(fov) Cart ID & Location Embedded Device Detection of SCID Recognition Shopper Path Z Y X Unique Shopping Cart ID (SCID) 7

8 Configuration details Camera Specifications a maximum of 2 megapixels resolution at 1FPS Environment (height, lighting, distance) height of camera is fixed at around 8 feet fluorescent light distance of cart from camera can be between 3-20 Feet SCID/Marker design specifications SCID contains a 3x3 rectangular grid of size is 6 * 6 with unique symbols Black border in SCID is used for detection and it is 0.5 thick Typical Retail Store with Shopping Carts 8

9 SCID Overview Symbols are easily differentiable and identifiable with combination of 3 primary colors Symbols have maximum separation in terms of shape such as Length and Breadth (e.g., just a ratio may suffice) Color (a small number of easily distinguishable colors, e.g., red and blue) Orientation along major axis (e.g., horizontal and vertical) Redundant coding to enable detection even in cases with occlusion SCID code book can support up to 1000 unique carts on a 3x3 grid A B C B C A C A B ID Color Shape Orientation 9 Green Square NA 5 Blue Rectangle 180 deg 0 Red Triangle NA Sample SCID = Code book example for decoding ID 509 9

10 Algorithm Overview Detection & Recognition Start Frame I = 1 Grab I th frame from camera DETECTION RECOGNITION I = I+1 Store Cart ID Entry 10

11 Algorithm Overview Tracking START Entry I = 1 Read Cart ID Entry I Entry(I) == END? no Is Valid Cart? WAIT yes no I = I+1 yes PLOT PATH 11

12 Occlusion scenarios Effect Sample Occlusion (Partial row & column) Occlusion (Entire row or column ) Strip Removal 12

13 Solution highlights Computer Vision based recognition system adopting a symbolic recognition system Simple encoding with high accuracy Derogatory effects tackled using redundancy and image enhancement techniques Considerable ease of implementation with no customer interaction Minimal hardware requirements (Porting to DSP Processor - planned) Low power/low resolution camera No change in present environment setup Completely automated 13

14 Why Matlab for SCID implementation? Enabled quick prototyping Simple to use interface Availability of ready to use advanced image processing library Easy for code walkthrough and debugging Easy to package as a stand alone solution 14

15 Matlab Toolbox used Image Processing Statistics Compiler Builder 15

16 Image Processing Techniques Used Morphological Operations Non Linear Filtering Image Segmentation Color Invariance Operations Feature Extraction and Analysis 16

17 Infosys SCID - Solution Benefits Cost reduction Cheaper vision based software solutions as it requires only low cost cameras Low maintenance Do not require frequent service for hardware devices Scalability Highly scalable as only camera need to be procured and installed for increasing the coverage Versatility Same camera set up can be used for other purpose like surveillance and safety etc Rich Analytics Analyses shopper behavior, buying patterns, store map, most visited aisles, time spent on products etc In-store analytics solution powered by Infosys SCID* in Matlab environment * patent pending 17

18 Q&A Thanks 18