Combining Online Clustering and Rank Pooling Dynamics for Action Proposals

Abstract

The action proposals problem consists in developing efficient and effective approaches to retrieve, from untrimmed long videos, those temporal segments which are likely to contain human actions. This is a fundamental task for any video analysis solution, which will struggle to detect activities in a large-scale video collection without the proposals step, needing hence to apply an action classifier at every time location, in a temporal sliding window strategy, a pipeline which is clearly unfeasible. While all previous action proposals solutions are supervised, we introduce here a novel strategy that works in an unsupervised fashion. We rely on an online agglomerative clustering algorithm to build an initial set of proposals/clusters. Then a novel filtering approach is proposed, which uses the dynamics of the proposals discovered by the clustering, to measure their actioness, and proceeds to filter them accordingly. Our experiments show that our model improves the supervised state-of-the-art approaches when the number of proposals is controlled.