无人货架场景下的人体关节点定位算法研究

Research on human joint point positioning algorithm under the scene of self-service shelves

在新零售场景下,无人货架上商品种类繁多,易受光照等外界因素干扰,且顾客手持商品时手部或身体会对商品关键信息形成遮挡,使得自然场景中仅采用图像识别算法不能满足无人货架应用需求。针对无人货架实际应用场景的特性,基于深度学习中人体关节点定位算法与图像分类算法对该场景中的手持商品识别提出了解决方案。首先,利用人体关节点定位算法准确定位顾客上身关节点;然后,用图像分类算法识别以手臂相关关节点为中心截取的包含商品主要特征的图像。为了提升算法的实用性,对卷积姿态机CPM从关节点定位的速度与精度2个方面进行改进,设计了L-CPM和EP-L-CPM算法,并采用公开数据集和实际无人货架场景人体姿态数据集验证算法性能。实验结果表明,所提出的算法能够准确、高效地定位人体关节点。

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.