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Current state-of-the-art human action recognition is focused on theclassification of temporally trimmed videos in which only one action occurs perframe. In this work we address the problem of action localisation and instancesegmentation in which multiple concurrent actions of the same class may besegmented out of an image sequence. We cast the action tube extraction as anenergy maximisation problem in which configurations of region proposals in eachframe are assigned a cost and the best action tubes are selected via two passesof dynamic programming. One pass associates region proposals in space and timefor each action category, and another pass is used to solve for the tube'stemporal extent and to enforce a smooth label sequence through the video. Inaddition, by taking advantage of recent work on action foreground-backgroundsegmentation, we are able to associate each tube with class-specificsegmentations. We demonstrate the performance of our algorithm on thechallenging LIRIS-HARL dataset and achieve a new state-of-the-art result whichis 14.3 times better than previous methods.

Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos