Interaction Recognition Based on Relative Spatial Positions Between Individuals

Interaction recognition between multiple individuals is crucial in many practical scenarios, particularly in sports analysis, yet remains challenging due to complex dynamics and directional ambiguity. Most existing methods primarily focus on single-person actions, which are insufficient for analyzing multi-person interactions. The SportsHHI dataset, designed specifically for human-human interactions in sports videos, provides detailed annotations but faces limitations from strict subject-object direction definitions, class imbalance, and inadequate encoding of relative spatial information. To overcome these issues, this study proposes three enhancements: (1) redefining relative spatial encoding to include global positional context, (2) introducing a directionality-agnostic evaluation method suitable for bidirectional interactions, and (3) employing focal loss to address class imbalance. Experimental results on SportsHHI demonstrate the effectiveness of the proposed improvements, achieving up to an increase 2.67 at mAP compared to baseline methods.