Abstract
Action segment detection is an important yet challenging problem, since we need to localize the proposals which contain an action instance in a long untrimmed video with arbitrary length and random position. This task requires us not only to find the precise moment of starting and ending of an action instance, but also to detect action instances as many as possible. We propose a new model Self-Adaptive Perception to address this problem. We predict the action boundaries by classifying start and end of each action as separate components, allowing our model to predict the starting and ending boundaries roughly and generate candidate proposals. We evaluate each candidate proposals by a novel and flexible architecture called Discriminator. It can extract enough semantic information and generate precise confidence score of whether a proposal contains an action within its region, which benefit from the self-adaptive architecture. We conduct solid and rich experiments on large dataset Activity-Net, the result shows that our method achieves a competitive performance, outperforming most published state-of-the-art method in the field. And further experiments demonstrate the effect of each module of our model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buch, S., Escorcia, V., Shen, C., Ghanem, B., Niebles, J.C: Sst: single-stream temporal action proposals. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 2911–2920 (2017)
Dai, X., Singh, B., Zhang, G., Davis, L.S., Chen, Y.Q.: Temporal context network for activity localization in videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5793–5802 (2017)
Escorcia, V., Caba Heilbron, F., Carlos Niebles, J., Ghanem, B.: Daps: deep action proposals for action understanding. In: European Conference on Computer Vision, pp. 768–784. Springer (2016)
Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933–1941 (2016)
Gao, J., Chen, K., Nevatia, R.: Ctap: complementary temporal action proposal generation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 68–83 (2018)
Gao, J., Yang, Z., Chen, K., Sun, C., Nevatia, R.: Turn tap: temporal unit regression network for temporal action proposals. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3628–3636 (2017)
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1243–1252. JMLR. org (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Lin, T., Zhao, X., Shou, Z.: Temporal convolution based action proposal: Submission to activitynet (2017). arXiv preprint arXiv:1707.06750 (2017)
Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: Bsn: boundary sensitive network for temporal action proposal generation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Shou, Z., Wang, D., Chang, S.F.: Temporal action localization in untrimmed videos via multi-stage cnns. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp. 568–576 (2014)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets. arXiv preprint arXiv:1507.02159 (2015)
Wang, L., et al.: Temporal segment networks for action recognition in videos. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2740–2755 (2018)
Yao, T., et al.: Msr asia msm at activitynet challenge 2017: trimmed action recognition, temporal action proposals and dense captioning events in videos. In: CVPR ActivityNet Challenge Workshop (2017)
Acknowledgments
This research is supported by Beijing Natural Science Foundation (No. L181010 and 4172054), National Key R&D Program of China (No. 2016YFB0801100).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, J., Li, K., Niu, X. (2022). Self-adaptive Perception Model for Action Segment Detection. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-80119-9_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80118-2
Online ISBN: 978-3-030-80119-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)