Current methods for human-object interaction detection often use the spatial relation between a human and an object as an interaction pattern. However, this strategy is relatively simple and has low discrimination in similar interactions. To solve this drawback, the spatial relation between skeletons and objects is proposed to model the interaction pattern and improve the detection accuracy. First, the skeleton-object interaction pattern image is extracted for each interaction proposal. Second, a deep neural network is applied to learn the interaction features from these images. Finally, the interaction feature is added to the human-object interaction detection network by a multistream structure. In the experiments, we evaluate the proposed method on the HICO-DET and V-COCO datasets. Experimental results show that the proposed method can achieve the best performance compared with state-of-art methods.

当前用于人-物体交互检测的方法经常使用人和物体之间的空间关系作为交互模式。但是，此策略相对简单，并且在相似的交互中具有较低的辨别力。为了解决这个缺点，提出了骨骼与物体之间的空间关系，以对交互模式进行建模并提高检测精度。首先，针对每个交互提议提取骨架-对象交互模式图像。其次，应用深度神经网络从这些图像中学习交互特征。最后，通过多流结构将交互功能添加到人-对象交互检测网络。在实验中，我们在HICO-DET和V-COCO数据集上评估了提出的方法。