Fine-grained Activity Recognition in Baseball Videos [article]

AJ Piergiovanni, Michael S. Ryoo
2018 arXiv   pre-print
In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch
more » ... eed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.
arXiv:1804.03247v1 fatcat:6rxnufsosfgfzifighabrz7qym