Action Detection by Implicit Intentional Motion Clustering

Explicitly using human detection and pose estimation has found limited success in action recognition problems. This may be due to the complexity in the articulated motion human exhibit. Yet, we know that action requires an actor and intention. This paper hence seeks to understand the spatiotemporal properties of intentional movement and how to capture such intentional movement without relying on challenging human detection and tracking. We conduct a quantitative analysis of intentional movement, and our findings motivate a new approach for implicit intentional movement extraction that is based on spatiotemporal trajectory clustering by leveraging the properties of intentional movement. The intentional movement clusters are then used as action proposals for detection. Our results on three action detection benchmarks indicate the relevance of focusing on intentional movement for action detection; our method significantly outperforms the state of the art on the challenging MSR-II multi-action video benchmark.