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Locating actions in long untrimmed videos has been a challenging problem invideo content analysis. The performances of existing action localizationapproaches remain unsatisfactory in precisely determining the beginning and theend of an action. Imitating the human perception procedure with observationsand refinements, we propose a novel three-phase action localization framework.Our framework is embedded with an Actionness Network to generate initialproposals through frame-wise similarity grouping, and then a Refinement Networkto conduct boundary adjustment on these proposals. Finally, the refinedproposals are sent to a Localization Network for further fine-grained locationregression. The whole process can be deemed as multi-stage refinement using anovel non-local pyramid feature under various temporal granularities. Weevaluate our framework on THUMOS14 benchmark and obtain a significantimprovement over the state-of-the-arts approaches. Specifically, theperformance gain is remarkable under precise localization with high IoUthresholds. Our proposed framework achieves mAP@IoU=0.5 of 34.2%.

Precise Temporal Action Localization by Evolving Temporal Proposals