Events defined by the interaction of objects in a scene often are of critical importance, yet such events are typically rare and available labeled examples insufficient to train a conventional deep model that performs well across expected object appearances. Most deep learning activity recognition models focus on global context aggregation and do not explicitly consider object interactions inside the video, potentially overlooking important cues relevant to interpreting activity in the scene. In this paper, we show that a new model for explicit representation of object interactions significantly improves deep video activity classification for driving collision detection. We propose a Spatio-Temporal Action Graph (STAG) network, which incorporates spatial and temporal relations of objects. The network is automatically learned from data, with a latent graph structure inferred for the task. As a benchmark to evaluate performance on collision detection tasks, we introduce a novel data set based on data obtained from real life driving collisions and near-collisions. This data set reflects the challenging task of detecting and classifying accidents in a richly varying but yet highly constrained setting, that is very relevant to the evaluation of autonomous driving and alerting systems. Our experiments confirm that our STAG model offers significantly improved results for collision activity classification.
Herzig et al. REF propose a Spatio-Temporal Action Graph (STAG) network to model the latent graph structure of spatial and temporal relations between objects.
Classifying Collisions with Spatio-Temporal Action Graph Networks
ArXiv
ArXiv
Computer Science