  Online action detection is a task with the aim of identifying ongoing actions
from streaming videos without any side information or access to future frames.
Recent methods proposed to aggregate fixed temporal ranges of invisible but
anticipated future frames representations as supplementary features and
achieved promising performance. They are based on the observation that human
beings often detect ongoing actions by contemplating the future vision
simultaneously. However, we observed that at different action progressions, the
optimal supplementary features should be obtained from distinct temporal ranges
instead of simply fixed future temporal ranges. To this end, we introduce an
adaptive features sampling strategy to overcome the mentioned variable-ranges
of optimal supplementary features. Specifically, in this paper, we propose a
novel Learning Action Progression Network termed LAP-Net, which integrates an
adaptive features sampling strategy. At each time step, this sampling strategy
first estimates current action progression and then decide what temporal ranges
should be used to aggregate the optimal supplementary features. We evaluated
our LAP-Net on three benchmark datasets, TVSeries, THUMOS-14 and HDD. The
extensive experiments demonstrate that with our adaptive feature sampling
strategy, the proposed LAP-Net can significantly outperform current
state-of-the-art methods with a large margin.
