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Current state-of-the-art human activity recognition is focused on theclassification of temporally trimmed videos in which only one action occurs perframe. We propose a simple, yet effective, method for the temporal detection ofactivities in temporally untrimmed videos with the help of untrimmedclassification. Firstly, our model predicts the top k labels for each untrimmedvideo by analysing global video-level features. Secondly, frame-level binaryclassification is combined with dynamic programming to generate the temporallytrimmed activity proposals. Finally, each proposal is assigned a label based onthe global label, and scored with the score of the temporal activity proposaland the global score. Ultimately, we show that untrimmed video classificationmodels can be used as stepping stone for temporal detection.

Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge