Abstract

Info

Author(s):: WAN Jinliang1; XIONG Qiliang1; LIU Yuan2; MO Jieyi1; CHEN Ying1; 1. School of Instrument Science and Optoelectronic Engineering, Nanchang Hangkong University, Nanchang 330063, China 2. Department of Rehabilitation, Childrens Hospital of Chongqing Medical University, Chongqing 400044, China

Abstract:: Abstract: How to effectively reduce the size of infant pose estimation network models is a key issue restricting the "home-use" of infant pose estimation technology. Therefore, a lightweight method for infant pose estimation in home scenarios is proposed. The method takes the lightweight network MobileNetV3 as the encoding backbone and utilizesa PixelShuffle up-sampling module in the decoder for reducing the quantity of model parameters. Meanwhile, coordinate attention mechanism is used to better capture location information and channel feature information, highlighting the feature information of small targets and occluded human keypoints. Besides, the parallel cross-correlation convolution is further modified to enhance the capability of feature information extraction. The methods performance is verified on the general pose estimation dataset (COCO) and the dedicated infant pose estimation dataset (SyRIP). The results show that, with a calculation volume (GFLOPs) of only 0.96, the method achieves average accuracies of 73.5% and 91.0% on COCO and SyRIP datasets, respectively, proving that it can significantly reduce the quantity of model parameters and calculation volume without sacrificing pose estimation accuracy. The proposed lightweight estimation model is expected to be deployed on home appliances such as smart terminals, thereby realizing intelligent estimation of abnormal infant poses in home scenarios.