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In this work, we first empirically find the recognition accuracy is highly correlated with the bounding box size of an actor, and thus higher resolution of actors contributes to better performance.
We confirm that most of the existing video datasets are statistically biased to only capture action videos from a limited number of countries.
Ranked #1 on Action Detection on Charades (using extra training data)
Procedural knowledge, which we define as concrete information about the sequence of actions that go into performing a particular procedure, plays an important role in understanding real-world tasks and actions.
We propose the Asynchronous Interaction Aggregation network (AIA) that leverages different interactions to boost action detection.
Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario.
The problem of Online Human Behaviour Recognition in untrimmed videos, aka Online Action Detection (OAD), needs to be revisited.
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning.