Progression-Guided Temporal Action Detection in Videos
Chongkai Lu Man-Wai Mak Ruimin Li Zheru Chi Hong Fu
We present a novel framework, Action Progression Network (APN), for temporal
action detection (TAD) in videos. The framework locates actions in videos by
detecting the action evolution process. To encode the action evolution, we
quantify a complete action process into 101 ordered stages (0\%, 1\%, ...,
100\%), referred to as action progressions. We then train a neural network to
recognize the action progressions. The framework detects action boundaries by
detecting complete action processes in the videos, e.g., a video segment with
detected action progressions closely follow the sequence 0\%, 1\%, ..., 100\%.
The framework offers three major advantages: (1) Our neural networks are
trained end-to-end, contrasting conventional methods that optimize modules
separately; (2) The APN is trained using action frames exclusively, enabling
models to be trained on action classification datasets and robust to videos
with temporal background styles differing from those in training; (3) Our
framework effectively avoids detecting incomplete actions and excels in
detecting long-lasting actions due to the fine-grained and explicit encoding of
the temporal structure of actions. Leveraging these advantages, the APN
achieves competitive performance and significantly surpasses its counterparts
in detecting long-lasting actions. With an IoU threshold of 0.5, the APN
achieves a mean Average Precision (mAP) of 58.3\% on the THUMOS14 dataset and
98.9\% mAP on the DFMAD70 dataset.