中圖分類(lèi)號(hào)： TP391
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2022.03.009
引用格式： 鄔春學(xué)，賀欣欣. 基于ResNet50對(duì)地震救援中人體姿態(tài)估計(jì)的研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(3)：50-58，70.

Research on human posture estimation in earthquake rescue based on ResNet50

Abstract： It was found that, the main reason for such a high number of deaths lies in the missing of prime rescue time. So rescue UAV can be used to recognize the behaviors of affected population automatically and analyze their status. Human pose estimation refers to the process of detecting humans′ joints and limbs in image, which plays a crucial role in human machine interaction and application of action recognition. However, due to the factors such as complex background and covering of limbs, it is very difficult to note the human joints and limbs in image. To address the issue, this paper proposed a model combining ResNet50 and convolutional pose machine(CPM). According to the model, image features are obtained by residual network and the dependence between joints is obtained by fine adjustment mechanism. Finally the key points aggregated are divided to the corresponding human body. Experiment shows that compared with other human pose estimation models, such model can enhance the effect of human post estimation under complex earthquake rescue scenario.

Key words : neural network；human pose estimation；ResNet50；part affinity fields；earthquake rescue