Survey of action detection and localization in space and time using deep learning
Survey of action detection and localization in space and time using deep learning
A Survey on Deep Learning-based Spatio-temporal Action Detection
arXiv paper abstract https://arxiv.org/abs/2308.01618
arXiv PDF paper https://arxiv.org/pdf/2308.01618.pdf
Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time.
It has become a particularly active area of research in computer vision because of its explosively emerging real-world applications, such as autonomous driving, visual surveillance, entertainment, etc.
... This paper provides a comprehensive review of the state-of-the-art deep learning-based methods for STAD.
Firstly, a taxonomy is developed to organize these methods.
Next, the linking algorithms, which aim to associate the frame- or clip-level detection results together to form action tubes, are reviewed.
Then, the commonly used benchmark datasets and evaluation metrics are introduced, and the performance of state-of-the-art models is compared ...
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