Actionness-Assisted Recognition of Actions

Ye Luo, Loong-Fah Cheong, An Tran; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3244-3252

We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality. These descriptors are mainly trajectory-based, measuring sudden changes, temporal synchrony, and repetitiveness. The actionness map can be used to localize actions in a way that is generic across action and agent types. Furthermore, it also groups interacting regions into a useful unit of analysis, which is crucial for recognition of actions involving interactions. We then implement an actionness-driven pooling scheme to improve action recognition performance. Experimental results on three datasets show the advantages of our method on both action detection and action recognition comparing with other state-of-the-art methods.