Abstract: This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.

Abstract: The fine-grained variations in product images are usually due to slight variations in text, size, and color of the package. Both marginal variations in image content and illumination poses an important challenge in product classification. This disclosure relates to a system and method for fine-grained classification of similar-looking products utilizing object-level and part-level information. The system simultaneously captures an object-level and part-level information of the product. The object-level classification score of the product is estimated with the trained RC-Net, a deep supervised convolutional autoencoder. For annotation-free modelling of part-level information of the product the discriminative part-proposal of the product is identified around the BRISK key points. An ordered sequence of the discriminative part-proposals and the product image, encoded using stacked convolutional LSTM network, estimates the part-level classification score.

Abstract: This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.

Abstract: Object recognition based estimation of planogram compliance provides an expected arrangement of products in shelves. Identifying whether a product is placed in an appropriate location of a shelf is a challenging task due to various real-time parameters associated with image capturing. In the present disclosure, an input image associated with shelf of a retail store is received and a product images are cropped. Further, a set of reference images stored in a database are scaled corresponding to the input image. Further, one or more composite matching scores are calculated based on normalized cross-correlation and shape based feature matching to obtain one or more probable product images associated with a location. Further, a Directed Acyclic Graph (DAG) is constructed based on the one or more composite scores and the one or more probable products. Finally, an optimal matching product image for a particular location is obtained from the DAG.

Abstract: Object recognition based estimation of planogram compliance provides an expected arrangement of products in shelves. Identifying whether a product is placed in an appropriate location of a shelf is a challenging task due to various real-time parameters associated with image capturing. In the present disclosure, an input image associated with shelf of a retail store is received and a product images are cropped. Further, a set of reference images stored in a database are scaled corresponding to the input image. Further, one or more composite matching scores are calculated based on normalized cross-correlation and shape based feature matching to obtain one or more probable product images associated with a location. Further, a Directed Acyclic Graph (DAG) is constructed based on the one or more composite scores and the one or more probable products. Finally, an optimal matching product image for a particular location is obtained from the DAG.

Abstract: A processor implemented system and method for identification of an activity performed by a subject based on sensor data analysis is described herein. In an implementation, the method includes capturing movements of the subject in real-time using a sensing device. At least one action associated with the subject is ascertained from a predefined set of actions. From the predefined set of actions, a plurality of actions can collectively form at least one activity. The ascertaining is based on captured movements of the subject and at least one predefined action rule. The at least one action rule is based on context-free grammar (CFG) and is indicative of a sequence of actions for occurrence of the at least one activity. Further, a current activity performed by the subject is dynamically determined, based on the at least one action and an immediately preceding activity, using a non-deterministic push-down automata (NPDA) state machine.

Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.

Abstract: A processor implemented system and method for identification of an activity performed by a subject based on sensor data analysis is described herein. In an implementation, the method includes capturing movements of the subject in real-time using a sensing device. At least one action associated with the subject is ascertained from a predefined set of actions. From the predefined set of actions, a plurality of actions can collectively form at least one activity. The ascertaining is based on captured movements of the subject and at least one predefined action rule. The at least one action rule is based on context-free grammar (CFG) and is indicative of a sequence of actions for occurrence of the at least one activity. Further, a current activity performed by the subject is dynamically determined, based on the at least one action and an immediately preceding activity, using a non-deterministic push-down automata (NPDA) state machine.

Abstract: Disclosed is a system and method for detecting a human in an image, and a corresponding activity. The image is captured, wherein the image comprises a plurality of pixels having gray scale information and a depth information. The image is segmented into a plurality of segments based upon the depth information. A connected component analysis is performed on a segment in order to segregate the one or more objects into noisy objects and candidate objects, the noisy objects are eliminated from the segment. A plurality of features are extracted from the candidate objects, and are evaluated using a Hidden Markov Model (HMM) model in order to determine the candidate objects as one of the human or non-human. The corresponding activity associated with the human is detected based on a depth value associated with each pixel corresponding to the candidate object in the image.