基于图结构和改进级联金字塔网络的人机交互检测算法

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qing Ye, Xiuju Xu, Rui Li, Yongmei Zhang
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引用次数: 0

摘要

针对图像中人机交互(HOI)信息和空间位置信息利用不足的问题,我们提出了一种基于图结构和改进级联金字塔的人机交互检测网络。该网络由三个分支组成,即图分支、人-物分支和人的姿势分支。在图分支中,我们提出了基于图的交互特征生成算法(GIFGA),以解决交互信息利用不足的问题。GIFGA 将人和物体作为节点,将它们之间的交互关系作为边,从而构建一个初始密集图模型。然后,通过遍历每个节点,更新图模型,生成最终的交互特征。在人体姿态分支中,我们提出了一种改进的级联金字塔网络(ICPN),以解决空间位置信息利用不足的问题。ICPN 可提取人体姿态特征,并将物体边界框和提取的人体姿态映射到全局特征图上,从而在全局范围内捕捉最具区分度的交互相关区域特征。最后,将三个分支的特征输入多层感知器(MLP)进行融合,然后进行识别分类。实验结果表明,我们的网络在 V-COCO 和 HICO-DET 数据集上的 mAP 分别达到了 54.93% 和 28.69%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human–object interaction detection algorithm based on graph structure and improved cascade pyramid network

Aiming at the problem of insufficient use of human–object interaction (HOI) information and spatial location information in images, we propose a human–object​ interaction detection network based on graph structure and improved cascade pyramid. This network is composed of three branches, namely, graph branch, human–object branch and human pose branch. In graph branch, we propose a Graph-based Interactive Feature Generation Algorithm (GIFGA) to address the inadequate utilization of interaction information. GIFGA constructs an initial dense graph model by taking humans and objects as nodes and their interaction relationships as edges. Then, by traversing each node, the graph model is updated to generate the final interaction features. In human pose branch, we propose an Improved Cascade Pyramid Network (ICPN) to tackle the underutilization of spatial location information. ICPN extracts human pose features and maps both the object bounding boxes and extracted human pose maps onto the global feature map to capture the most discriminative interaction-related region features within the global context. Finally, the features from the three branches are fed into a Multi-Layer Perceptron (MLP) for fusion and then classified for recognition. Experimental results demonstrate that our network achieves mAP of 54.93% and 28.69% on the V-COCO and HICO-DET datasets, respectively.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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