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Summarizing video content is an important task in many applications. Thistask can be defined as the computation of the ordered list of actions presentin a video. Such a list could be extracted using action detection algorithms.However, it is not necessary to determine the temporal boundaries of actions toknow their existence. Moreover, localizing precise boundaries usually requiresdense video analysis to be effective. In this work, we propose to directlycompute this ordered list by sparsely browsing the video and selecting oneframe per action instance, task known as action spotting in literature. To dothis, we propose ActionSpotter, a spotting algorithm that takes advantage ofDeep Reinforcement Learning to efficiently spot actions while adapting itsvideo browsing speed, without additional supervision. Experiments performed ondatasets THUMOS14 and ActivityNet show that our framework outperforms state ofthe art detection methods. In particular, the spotting mean Average Precisionon THUMOS14 is significantly improved from 59.7% to 65.6% while skipping 23% ofvideo.

ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos