Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots

• URL: http://arxiv.org/abs/2310.14063v1
• Date: Sat, 21 Oct 2023 16:49:23 GMT
• Title: Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots
• Authors: Aditya Kapoor, Vartika Sengar, Nijil George, Vighnesh Vatsal, Jayavardhana Gubbi, Balamuralidhar P and Arpan Pal
• Abstract summary: Co-AD is a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) It is able to flag misplaced objects without using a prior knowledge base such as a planogram. It has a peak success rate of 89.90% on anomaly detection image sets of retail objects.
• Score: 3.989104441591223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.

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