  When we say a person is texting, can you tell the person is walking or
sitting? Emphatically, no. In order to solve this incomplete representation
problem, this paper presents a sub-action descriptor for detailed action
detection. The sub-action descriptor consists of three levels: the posture, the
locomotion, and the gesture level. The three levels give three sub-action
categories for one action to address the representation problem. The proposed
action detection model simultaneously localizes and recognizes the actions of
multiple individuals in video surveillance using appearance-based temporal
features with multi-CNN. The proposed approach achieved a mean average
precision (mAP) of 76.6% at the frame-based and 83.5% at the video-based
measurement on the new large-scale ICVL video surveillance dataset that the
authors introduce and make available to the community with this paper.
Extensive experiments on the benchmark KTH dataset demonstrate that the
proposed approach achieved better performance, which in turn boosts the action
recognition performance over the state-of-the-art. The action detection model
can run at around 25 fps on the ICVL and more than 80 fps on the KTH dataset,
which is suitable for real-time surveillance applications.
