Finding Causal Interactions in Video Sequences

Mustafa Ayazoglu, Burak Yilmaz, Mario Sznaier, Octavia Camps; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 3575-3582

This paper considers the problem of detecting causal interactions in video clips. Specifically, the goal is to detect whether the actions of a given target can be explained in terms of the past actions of a collection of other agents. We propose to solve this problem by recasting it into a directed graph topology identification, where each node corresponds to the observed motion of a given target, and each link indicates the presence of a causal correlation. As shown in the paper, this leads to a block-sparsification problem that can be efficiently solved using a modified Group-Lasso type approach, capable of handling missing data and outliers (due for instance to occlusion and mis-identified correspondences). Moreover, this approach also identifies time instants where the interactions between agents change, thus providing event detection capabilities. These results are illustrated with several examples involving non-trivial interactions amongst several human subjects.