  Temporal action proposal generation is an important yet challenging problem,
since temporal proposals with rich action content are indispensable for
analysing real-world videos with long duration and high proportion irrelevant
content. This problem requires methods not only generating proposals with
precise temporal boundaries, but also retrieving proposals to cover truth
action instances with high recall and high overlap using relatively fewer
proposals. To address these difficulties, we introduce an effective proposal
generation method, named Boundary-Sensitive Network (BSN), which adopts "local
to global" fashion. Locally, BSN first locates temporal boundaries with high
probabilities, then directly combines these boundaries as proposals. Globally,
with Boundary-Sensitive Proposal feature, BSN retrieves proposals by evaluating
the confidence of whether a proposal contains an action within its region. We
conduct experiments on two challenging datasets: ActivityNet-1.3 and THUMOS14,
where BSN outperforms other state-of-the-art temporal action proposal
generation methods with high recall and high temporal precision. Finally,
further experiments demonstrate that by combining existing action classifiers,
our method significantly improves the state-of-the-art temporal action
detection performance.
