Abstract
Human action quality assessment (AQA) recently has attracted increasing attentions in computer vision for its practical applications, such as skill training, physical rehabilitation and scoring sports events. In this paper, we propose a partially connected LSTM with triplet losses to evaluate different skill levels. Compared to human action recognition (HAR), we explain and discuss two characteristics and countermeasures of AQA. To ignore the negative influence of complex joint movements in actions, the skeleton is not regarded as a single graph. The fully connected layer in the LSTM model is replaced by the partially connected layer, using a diagonal matrix which activates the corresponding weights, to explore hierarchical relations in the skeleton graph. Furthermore, to improve the generalization ability of models, we introduce additional functions of triplet loss to the loss function, which make samples with similar skill levels close to each other. We carry out experiments to test our model and compare it with seven LSTM architectures and three GNN architectures on the UMONS-TAICHI dataset and walking gait dataset. Experimental results demonstrate that our model achieves outstanding performance.
Supported by the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 202100009, and the Fundamental Research Funds for Central Universities No. 2021TD006.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McNally, W., Vats, K., Pinto, T., et al.: GolfDB: a video database for golf swing sequencing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Szczęsna, A., Błaszczyszyn, M., Pawlyta, M.: Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes. Sci. Data 8(1), 1–12 (2021)
Tits, M., Laraba, S., Caulier, E., et al.: UMONS-TAICHI: a multimodal motion capture dataset of expertise in Taijiquan gestures. Data Brief 19, 1214–1221 (2018)
Liao, Y., Vakanski, A., Xian, M.: A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans. Neural Syst. Rehabil. Eng. 28(2), 468–477 (2020)
Capecci, M., Ceravolo, M.G., Ferracuti, F., et al.: The KIMORE dataset: KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 27(7), 1436–1448 (2019)
Xu, C., Fu, Y., Zhang, B., et al.: Learning to score figure skating sport videos. IEEE Trans. Circuits Syst. Video Technol. 30(12), 4578–4590 (2019)
Parmar, P., Morris, B.T.: What and how well you performed? A multitask learning approach to action quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 304–313 (2019)
Parmar, P., Tran Morris, B.: Learning to score olympic events. In: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition Workshops, pp. 20–28 (2017)
Parmar, P., Morris, B.: Action quality assessment across multiple actions. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1468–1476. IEEE (2019)
Pan, J.H., Gao, J., Zheng, W.S.: Action assessment by joint relation graphs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6331–6340 (2019)
Li, H.Y., Lei, Q., Zhang, H.B., et al.: Skeleton based action quality assessment of figure skating videos. In: 2021 11th International Conference on Information Technology in Medicine and Education (ITME), pp. 196–200. IEEE (2021)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Nguyen, T.N., Huynh, H.H., Meunier, J.: 3D reconstruction with time-of-flight depth camera and multiple mirrors. IEEE Access 6, 38106–38114 (2018)
Li, Z., Huang, Y., Cai, M., et al.: Manipulation-skill assessment from videos with spatial attention network. In: Proceedings of the IEEE/CVF International Conference on Computer 14Vision Workshops (2019)
Gao, Y., Vedula, S.S., Reiley, C.E., et al.: JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: MICCAI Workshop: M2CAI, vol. 3, p. 3 (2014)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-Second AAAI Conference on Artificial In-telligence (2018)
Shi, L., Zhang, Y., Cheng, J., et al.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026–12035 (2019)
Song, S., Lan, C., Xing, J., et al.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1 (2017)
Chen, Y., Zhang, Z., Yuan, C., et al.: Channel-wise topology refinement graph convolution for skeleton-based action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13359–13368 (2021)
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110–1118 (2015)
Thakkar, K., Narayanan, P.J.: Part-based graph convolutional network for action recognition. arXiv preprint arXiv:1809.04983 (2018)
Si, C., Chen, W., Wang, W., et al.: An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227–1236 (2019)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Prétet, L., Richard, G., Peeters, G.: Learning to rank music tracks using triplet loss. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 511–515. IEEE (2020)
Shi, L., Zhang, Y., Cheng, J., et al.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912–7921 (2019)
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
Wang, X., Li, J., Hu, H. (2022). Skeleton-Based Action Quality Assessment via Partially Connected LSTM with Triplet Losses. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-18913-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18912-8
Online ISBN: 978-3-031-18913-5
eBook Packages: Computer ScienceComputer Science (R0)