Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction

TL;DR

This paper introduces a novel approach for predicting human interactions by learning structural context models with LSTM networks and fusing their scores with spatial-temporal information, improving accuracy in video analysis.

Contribution

It proposes a new method combining structural context learning via LSTMs with a ranking score fusion technique for enhanced interaction prediction.

Findings

Improved prediction accuracy on BIT Interaction dataset.

Effective modeling of global and local interaction contexts.

Demonstrated benefits over existing methods.

Abstract

Predicting an interaction before it is fully executed is very important in applications such as human-robot interaction and video surveillance. In a two-human interaction scenario, there often contextual dependency structure between the global interaction context of the two humans and the local context of the different body parts of each human. In this paper, we propose to learn the structure of the interaction contexts, and combine it with the spatial and temporal information of a video sequence for a better prediction of the interaction class. The structural models, including the spatial and the temporal models, are learned with Long Short Term Memory (LSTM) networks to capture the dependency of the global and local contexts of each RGB frame and each optical flow image, respectively. LSTM networks are also capable of detecting the key information from the global and local interaction contexts. Moreover, to effectively combine the structural models with the spatial and temporal models for interaction prediction, a ranking score fusion method is also introduced to automatically compute the optimal weight of each model for score fusion. Experimental results on the BIT Interaction and the UT-Interaction datasets clearly demonstrate the benefits of the proposed method.