title: Customer tracking in supermarket using artificial intelligence
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐ¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³ ÐºÐ»Ð¸ÐµÐ½ÑÐ¾Ð² Ð² ÑÑÐ¿ÐµÑÐ¼Ð°ÑÐºÐµÑÐµ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°
Creators: name: ÐÐ¾Ð±Ð°ÑÐµÑ, ÐÑÑÐ°Ð¼Ð¼ÐµÐ´
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
supermarket
customer tracking
ultiplehead tracking
ÑÑÐ¿ÐµÑÐ¼Ð°ÑÐºÐµÑ
Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ ÐºÐ»Ð¸ÐµÐ½ÑÐ¾Ð²
Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¿Ð¾ÑÐµÑÐ¸ÑÐµÐ»ÐµÐ¹

description: The study investigates the application of computer vision techniques within the retail sector, with a specific focus on customer tracking methodologies. The accurate, precise, and efficient detection of customers in a supermarket is essential and important for strategic retail planning. Recognizing how customers travel around a shop and engage with merchandise is crucial in retail settings. Even though the retail setting has many advantageous features that aid computer vision, such as adequate lighting, the variety and quantity of products offered, along with the possibility of ambiguity in the movements of customers, make it difficult to precisely measure customer behavior. In order to learn customers abilities based on occlusion-free RGB-D cameras in a top-view configuration, machine-learning and feature-based technologies for people counting, interactions analysis, and re-identification have been created during the last years. Nevertheless, machine learning techniques developed into deep learning techniques as we entered the multimedia big data age. These techniques are more potent and effective at handling the intricacies of human behavior. Utilizing a dataset captured by a camera situated in an office environment, participants enact diverse customer behaviors commonly observed in supermarket settings. A practical demonstration is provided, illustrating the implementation of this dataset to track participants, with particular emphasis on the integration of a head tracking model to address occlusion-related challenges. In addition, a new model is presented that aims to distinguish between employees and consumers based on their unique gaits.Â A real-world dataset gathered over the course of a 24-hour period at a busy supermarket is used to evaluate this model. Our results show 98% training accuracy and 93% assessment accuracy, highlighting the effectiveness of the suggested method in retail consumer monitoring applications.
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