Streaming Video Temporal Action Segmentation In Real Time

• URL: http://arxiv.org/abs/2209.13808v3
• Date: Tue, 10 Oct 2023 03:00:48 GMT
• Title: Streaming Video Temporal Action Segmentation In Real Time
• Authors: Wujun Wen, Yunheng Li, Zhuben Dong, Lin Feng, Wanxiao Yang, Shenlan Liu
• Abstract summary: We propose a real-time end-to-end multi-modality model for streaming video real-time temporal action segmentation task. Our model segments human action in real time with less than 40% of state-of-the-art model computation and achieves 90% of the accuracy of the full video state-of-the-art model.
• Score: 2.8728707559692475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies follow a pattern that builds models based on features instead of raw video picture information. However, we claim those models are trained complicatedly and limit application scenarios. It is hard for them to segment human actions of video in real time because they must work after the full video features are extracted. As the real-time action segmentation task is different from TAS task, we define it as streaming video real-time temporal action segmentation (SVTAS) task. In this paper, we propose a real-time end-to-end multi-modality model for SVTAS task. More specifically, under the circumstances that we cannot get any future information, we segment the current human action of streaming video chunk in real time. Furthermore, the model we propose combines the last steaming video chunk feature extracted by language model with the current image feature extracted by image model to improve the quantity of real-time temporal action segmentation. To the best of our knowledge, it is the first multi-modality real-time temporal action segmentation model. Under the same evaluation criteria as full video temporal action segmentation, our model segments human action in real time with less than 40% of state-of-the-art model computation and achieves 90% of the accuracy of the full video state-of-the-art model.

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