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Temporally smooth online action detection using cycle-consistent future anticipation

2021-04-16 11:00:19
Young Hwi Kim, Seonghyeon Nam, Seon Joo Kim

Abstract

tract: Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end. However, many real-world problems require the online setting, making a decision immediately using only the current and the past frames of videos such as in autonomous driving and surveillance systems. In this paper, we present a novel solution for online action detection by using a simple yet effective RNN-based networks called the Future Anticipation and Temporally Smoothing network (FATSnet). The proposed network consists of a module for anticipating the future that can be trained in an unsupervised manner with the cycle-consistency loss, and another component for aggregating the past and the future for temporally smooth frame-by-frame predictions. We also propose a solution to relieve the performance loss when running RNN-based models on very long sequences. Evaluations on TVSeries, THUMOS14, and BBDB show that our method achieve the state-of-the-art performances compared to the previous works on online action detection.

Abstract (translated)

URL

https://arxiv.org/abs/2104.08030

PDF

https://arxiv.org/pdf/2104.08030


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