Effective Action Detection Using Temporal Context and Posterior Probability of Length

Xinran Liu, Yan Song, Jinhui Tang

Abstract: In this paper, we focus on human action detection for untrim-med long videos. We propose an effective action detection system aiming at solving two difficulties in existing works. Firstly, we propose to take into account the temporal context information in model learning to tackle with the problem of high-quality proposal generation. Secondly, we propose to utilize the posterior probability of proposal length to adjust the selection criterion of action proposals. This can effectively encourage the proposals with reasonable lengths and suppress the high-classification-score proposals with unreasonable lengths. We test our method on the THUMOS14 Dataset and the experiment results show that our action detection system improve the performance by about 4% compared with the state-of-art methods.