【摘要】 调查发现，地震中死亡人数增加的原因主要是错过救援的黄金时间，因此可通过救援无人机自动对受灾人员进行行为识别与状态分析。人体姿态估计是指对图像中人体关节点和肢体进行检测的过程，在人机交互和行为识别应用中起着重要的作用，然而由于背景复杂、肢体被遮挡等因素导致标注人体关节点和肢体十分困难。因此提出一种结合ResNet50及CPM的模型，该模型通过获取图像特征和精调机制，计算出关节点依赖关系，最后划分到对应人体。实验表明，该模型与其他模型对比能够提高复杂场景下人体姿态估计的效果。更多还原

【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 back-ground 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 earth-quake rescue scenario.更多还原