# Planogram Compliance Control via Object Detection, Sequence Alignment, and Focused Iterative Search

arXiv: https://arxiv.org/abs/2212.01004 · HTML: https://mlsanity.com/paper/2212.01004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
- [The Freiburg Groceries Dataset](https://mlsanity.com/paper/1611.05799/) (2016)
- [Product recognition in store shelves as a sub-graph isomorphism problem](https://mlsanity.com/paper/1707.08378/) (2017)
- [Planogram Compliance Checking Based on Detection of Recurring Patterns](https://mlsanity.com/paper/1602.06647/) (2016)

## Built on by (most cited first, 1 of 1)

- [Embedded Planogram Compliance Control System](https://mlsanity.com/paper/2401.06690/) (2024)

## Abstract (arXiv)

Smart retail stores are becoming the fact of our lives. Several computer vision and sensor based systems are working together to achieve such a complex and automated operation. Besides, the retail sector already has several open and challenging problems which can be solved with the help of pattern recognition and computer vision methods. One important problem to be tackled is the planogram compliance control. In this study, we propose a novel method to solve it. The proposed method is based on object detection, planogram compliance control, and focused and iterative search steps. The object detection step is formed by local feature extraction and implicit shape model formation. The planogram compliance control step is formed by sequence alignment via the modified Needleman-Wunsch algorithm. The focused and iterative search step aims to improve the performance of the object detection and planogram compliance control steps. We tested all three steps on two different datasets. Based on these tests, we summarize the key findings as well as strengths and weaknesses of the proposed method.