  We propose a single-stage Human-Object Interaction (HOI) detection method
that has outperformed all existing methods on HICO-DET dataset at 37 fps on a
single Titan XP GPU. It is the first real-time HOI detection method.
Conventional HOI detection methods are composed of two stages, i.e.,
human-object proposals generation, and proposals classification. Their
effectiveness and efficiency are limited by the sequential and separate
architecture. In this paper, we propose a Parallel Point Detection and Matching
(PPDM) HOI detection framework. In PPDM, an HOI is defined as a point triplet <
human point, interaction point, object point>. Human and object points are the
center of the detection boxes, and the interaction point is the midpoint of the
human and object points. PPDM contains two parallel branches, namely point
detection branch and point matching branch. The point detection branch predicts
three points. Simultaneously, the point matching branch predicts two
displacements from the interaction point to its corresponding human and object
points. The human point and the object point originated from the same
interaction point are considered as matched pairs. In our novel parallel
architecture, the interaction points implicitly provide context and
regularization for human and object detection. The isolated detection boxes are
unlikely to form meaning HOI triplets are suppressed, which increases the
precision of HOI detection. Moreover, the matching between human and object
detection boxes is only applied around limited numbers of filtered candidate
interaction points, which saves much computational cost. Additionally, we build
a new application-oriented database named HOI-A, which severs as a good
supplement to the existing datasets. The source code and the dataset will be
made publicly available to facilitate the development of HOI detection.
