Real-life violent social interaction detection
This paper proposes a method to detect and localize dyadic human interactions in real videos. The idea stems from the significant difference between an action performed by a single subject and an interaction between two persons. In the first case all the visual information is concentrated on the subject, while in the latter case the action of a person is related to the interacting person's attitude, following an action/reaction principle. This kind of behavior is significant especially in natural and real scenarios, in which people are moving freely without the awareness of being recorded. To highlight these features and provide researchers with a common ground for comparisons, we have collected and annotated a new dataset, retrieving from YouTube 30 different videos of a specific type of interaction, namely urban fight situations. The proposed dataset is one of the most challenging annotated video collection concerning dyadic interactions, due to the intrinsic intra-class variability characterizing real fights. In addition, we provide an extensive experimental analysis on this dataset and we demonstrate that the visual information extracted in the area associated to the interpersonal space plays a fundamental role in detecting fights.