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Recurrent Tubelet Proposal and Recognition Networks for Action Detection
[chapter]
2018
Lecture Notes in Computer Science
Detecting actions in videos is a challenging task as video is an information intensive media with complex variations. Existing approaches predominantly generate action proposals for each individual frame or fixed-length clip independently, while overlooking temporal context across them. Such temporal contextual relations are vital for action detection as an action is by nature a sequence of movements. This motivates us to leverage the localized action proposals in previous frames when
doi:10.1007/978-3-030-01231-1_19
fatcat:fllf4ihw6vawvpfns3cffulgle