BLP-Boundary likelihood pinpointing networks for accurate temporal action localization

作     者：Kong, Weijie Li, Nannan Liu, Shan Li, Thomas Li, Ge 

作者机构：School of Electronic and Computer Engineering Peking University Shenzhen Peng Cheng Laboratory Shenzhen China Media Lab Tencent Shenzhen Advanced Institute of Information Technology Peking University Hangzhou China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2018年

核心收录：

主　　题：Regression analysis 

摘      要：Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enough information to yield accurate temporal boundaries. In this paper, we propose a novel Boundary Likelihood Pinpointing (BLP) network to alleviate this deficiency of boundary regression and improve the localization accuracy. Given a loosely localized search interval that contains an action instance, BLP casts the problem of localizing temporal boundaries as that of assigning probabilities on each equally divided unit of this interval. These generated probabilities provide useful information regarding the boundary location of the action inside this search interval. Based on these probabilities, we introduce a boundary pinpointing paradigm to pinpoint the accurate boundaries under a simple probabilistic framework. Compared with other C3D feature based detectors, extensive experiments demonstrate that BLP significantly improves the localization performance of recent state-of-the-art detectors, and achieves competitive detection mAP on both THUMOS 14 and ActivityNet datasets, particularly when the evaluation tloU is high. Copyright © 2018, The Authors. All rights reserved.