Open AccessDistance-based Human-Object Interaction Detection
Author(s) -
Weifeng Li,
Hongbing Yang,
Lei Zhou,
Dawei Niu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1920/1/012073
Subject(s) - computer science , object (grammar) , artificial intelligence , process (computing) , pattern recognition (psychology) , computer vision , object detection , measure (data warehouse) , feature (linguistics) , object based spatial database , spatial analysis , data mining , geography , spatial database , remote sensing , linguistics , philosophy , operating system
In our lives, the interactions between humans and objects around them are happening all the time. The purpose of human-object interaction(HOI) detection is to locate humans and objects in a visual scene and infer the type of interaction between the two. Most of the HOI detection works infer the interaction type from two aspects: spatial features and visual features. We adopt the same strategy as them, but in the stage of extracting spatial features, we believe that the relationships between human body parts and objects are very important. We choose distance as a standard to measure the relationship between body parts and objects, and add it to the process of extracting spatial features, then use the refined spatial features to further extract visual features. Our experimental results on the V-COCO dataset show that our new model based on distance is effective. Compared with other methods, the accuracy of our model is significantly improved.