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arXiv · 1702.05448

Learning to Detect Human-Object Interactions

Yu-Wei Chao·Yunfan Liu·Xieyang Liu·Huayi Zeng·Jia Deng
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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.

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Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, Jia Deng. 2017-02-17. Learning to Detect Human-Object Interactions. https://arxiv.org/abs/1702.05448

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