# Planogram Compliance Checking Based on Detection of Recurring Patterns

arXiv: https://arxiv.org/abs/1602.06647 · HTML: https://mlsanity.com/paper/1602.06647/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2016.

## Built on by (most cited first, 4 of 4)

- [An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores](https://mlsanity.com/paper/2202.13081/) (2022)
- [Planogram Compliance Control via Object Detection, Sequence Alignment, and Focused Iterative Search](https://mlsanity.com/paper/2212.01004/) (2022)
- [Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots](https://mlsanity.com/paper/2310.14063/) (2023)
- [Embedded Planogram Compliance Control System](https://mlsanity.com/paper/2401.06690/) (2024)

## Abstract (arXiv)

In this paper, a novel method for automatic planogram compliance checking in retail chains is proposed without requiring product template images for training. Product layout is extracted from an input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram to measure the level of compliance. A divide and conquer strategy is employed to improve the speed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region respectively and then merged together to estimate the product layout. Experimental results on real data have verified the efficacy of the proposed method. Compared with a template-based method, higher accuracies are achieved by the proposed method over a wide range of products.