| 摘要 |
A method for automatically constructing a planogram from photographs of shelving, replacing laborious manual construction includes the following steps: a step (1) in which the images are received, a step (2) in which the images are assembled, a step (3) in which the structure is automatically constructed, a step (4) in which the products are automatically detected, and a step (5) in which the products are positioned in the structure. The product detection step (4) enhances traditional image recognition techniques, using artificial learning techniques to incorporate characteristics specific to the planograms. This product detection step (4) also includes at least two successive classification steps, namely: an initialization step (41) with detection of product categories; and a classification step (42) with the classification of the products themselves, each of these steps including a first image recognition step, followed by a statistical filtering step based on the characteristics specific to the planograms. |
| 主权项 |
1. A method for automatic construction of a planogram, comprising:
receiving one or more shelving images, assembling said images, in the case where there are several, automatic construction of a structure constituting the planogram, automatic recognition of products contained in the images, positioning the products in the planogram according to the results of the previous automatic recognition of the products and the results of the previous detection of the structure, wherein the automatic recognition step comprises at a minimum: a) an initial recognition step, resulting in a set of possibilities, each possibility being in a form of an ordered pair consisting of a hypothesis and a a probability, called a detection probability, that said hypothesis is true, in which the hypothesis is an ordered pair consisting of a position in the image and an identified product, wherein this initial recognition step consists of a first classification step for classifying products according to several categories, wherein the number of categories is less than the number of products, and a first filtering step of global filtering of these results by re-evaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating information specific to the products, and b) a second recognition step, using the resulting set of possibilities from the original recognition step to make a new detection, wherein this second recognition step consists of a second classification step based on matchings of points of interest, and a second filtering step at the planogram level that consists of reevaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating global information on a context previously estimated on a base of planograms, each of the first classification step, first filtering step, second classification step and second filtering step being followed immediately by a selection step of selecting the best candidates, each selection step respectively corresponding to a thresholding of the detection probabilities. |
