MULTILEVEL INTERACTION REASONING FOR COMPLEX EVENT RECOGNITION

Event as a complex process contains many factors. Objects, environments, and their interactions vary with time. Recognizing event remains a challenging task in computer vision. In this paper, a multilevel interaction reasoning framework is proposed for complex event recognition. Firstly, we construct a 3D ConvNet to extract the spatial-temporal feature to present global scene. Then a graph ConvNet is built to reasoning about the multilevel interaction: object-object and object-environment interaction, via graphs that contain object feature in video, and projection of global scene feature in 3D ConvNet. The proposed method effectively explore the nature of events occurring and developing, and experimental results on challenging UCF-Crime dataset achieve state-of-the-art with a 3.5\% gain over other models.