Two-person interaction recognition via spatial multiple instance embedding

• A MI-based framework for two-person interaction recognition in videos.
• Relative distances between people are encoded within MI-learning.
• Two-person features are utilized in spatial multiple instance embedding.
• Our framework receives on par or better results than the state-of-the-art.

In this work, we look into the problem of recognizing two-person interactions in videos. Our method integrates multiple visual features in a weakly supervised manner by utilizing an embedding-based multiple instance learning framework. In our proposed method, first, several visual features that capture the shape and motion of the interacting people are extracted from each detected person region in a video. Then, two-person visual descriptors are formed. Since the relative spatial locations of interacting people are likely to complement the visual descriptors, we propose to use spatial multiple instance embedding, which implicitly incorporates the distances between people into the multiple instance learning process. Experimental results on two benchmark datasets validate that using two-person visual descriptors together with spatial multiple instance learning offers an effective way for inferring the type of the interaction.