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Recognition of garments is a complex image processing task, which benefits a wide array of applications such as customer behavior analyses, forecasting sales, market segmentation [1], and computer-aided designs for fashion [2–4]. This task has attracted a lot of research interest in the field of image processing [5–8]. Identifying the garments that customers of a store are interested in can further aid the development of the aforementioned use cases.

As a retailer, understanding the needs of the customers and anticipating the future needs of the customer base can be beneficial in aiding to stock up on appealing products. This can prove vital to a growing business strategy in today’s ever so competitive world. The illustrated prospects in this research area motivated us to propose an approach that detects garments from surveillance videos and also indicates the extent to which a person is interested in a particular garment. We believe that the usefulness of analyzing customer behavior using machine learning shows promise for a wide range of applications, especially in those where stakeholders can benefit from these analyses. It is noteworthy that the task of garment detection has attracted research attention in the field of image processing, however, developing a system to recognize garments that appeal to customers from surveillance videos is a herculean challenge primarily due to several reasons as explained further.

Firstly, there are numerous sub-tasks involved which include customer detection, tracking, and clothing segmentation. Secondly, the identification of complex garments from indoor surveillance footage is complicated as these garments usually comprise of different textures, types of fabric, and a multitude of colors in addition to their deformable nature. Finally, as CCTV cameras are conveniently installed in stores at angles that make them suitable to monitor human behavior, many a time these customers themselves behave as occlusions by blocking regions of garments to be detected, thereby hindering the task of identifying the garments themselves, further escalating the complications.

In this chapter, we propose a novel framework for the task of identifying garments that appeal to customers and these garments are referred to as garments of interest. After the identification of garments in a video frame using background subtraction, we employ the Mask R-CNN object detection model [9] for the task of identifying customers in the store. We use the OpenPose pose estimation framework [10] to obtain the feature points on the human body, enabling us to draw a correlation between a customer’s wrist coordinates and a garment that the customer most recently interacted with. This allows us to derive a confidence score metric between a customer and the garment into consideration.

The chapter is organized as follows: Section 3.2 highlights the related works, Section 3.3 elucidates the proposed approach, Section 3.4 highlights the results obtained by the proposed approach, Section 3.5 highlights the major findings of this study, and Section 3.6 delineates the conclusion and scope for future work in this field.