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

TL;DR

This paper presents a novel computer vision-based method for planogram compliance control in retail stores, combining object detection, sequence alignment, and iterative search to improve accuracy and robustness.

Contribution

The study introduces an integrated approach that combines local feature-based object detection with sequence alignment and focused iterative search for enhanced planogram compliance assessment.

Findings

Effective in two different datasets

Improves detection and compliance accuracy

Identifies strengths and weaknesses of the method

Abstract

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.