Supervised learning for Out-of-Stock detection in panoramas of retail shelves

Improving inventory management is essential to retailer profitability. This paper proposes a supervised learning approach for Out-of-Stock (OOS) detection by Texture, Color and Geometry features in high-resolution panoramic images of grocery retail shelves. Cascade classifiers are used to detect labels that can potentially be used to confirm the presence of the OOS cases. The image acquisition setup includes a camera cart that shoots from multi-viewpoints aiming a parallel motion to the shelf. The correction of perspective distortion is applied to handle the different camera translation motions while stitching together images with a high-level of similarity. From the generated panoramas, the proposed OOS detection is followed by classification with Support Vector Machines. The experimental tests were performed throughout the retail environment with real data obtained from supermarket shelves containing labels near the visible ruptures. Results show a detection accuracy of 84.5% for OOS and a sensitivity of 86.6% for label detection.