Abstract: In the new retail scene, there are a large variety of products on self-service shelves, which are susceptible to external factors such as light. In addition, customers' hands or bodies will block the key information of products when they hold the products. Therefore, only using the image recognition algorithm in the natural scene cannot meet the application requirements of the self-service shelves. 
Aim- ing at the characteristics of the actual application scene of self-service shelves, a handheld products re-cognition solution is proposed based on the human joint point positioning algorithm and the image classification algorithm in deep learning. Firstly, the human joint point positioning algorithm is used to accurately locate the joint points of the upper body of the customers. Secondly, the image classification algorithm mainly identifies the images containing the main features of the products, which are centered on the joint points of the arm. In order to improve the practicality of the algorithm, L-CPM and EP-L-CPM are designed to improve the Convolutional Pose Machine (CPM) from the two aspects of the speed and accuracy of joint point positioning. The public dataset and the human posture dataset of actual self- service shelves scene are used to verify the performance of algorithms. Experimental results show that the proposed algorithm can accurately and efficiently locate the human body joints.

Key words: deep learning, self-service shelves, CPM, speed, accuracy