Learning to Detect Human-Object Interactions

Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, Jia Deng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

74 Scopus citations

Abstract

We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.
Original languageEnglish (US)
Title of host publication2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE
Pages381-389
Number of pages9
ISBN (Print)9781538648865
DOIs
StatePublished - May 7 2018
Externally publishedYes

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