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TNUE-Fight Detection: A new challenge benchmark for Fighting Recognition

Năm XB 2023 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 848 Đơn vị NT&TT DOI / Link https://doi.org/10.1007/978-3-031-50818-9_34 ↗

Tác giả

Trung-Nghia Phung

Tóm tắt

Today, violence is one of the most common abnormal actions that need to be monitored and detected early. Therefore, a violence recognition system is very necessary and has great practical significance. In recent years, deep learning has achieved remarkable achievements in various different problems, however, deep learning models for violent action recognition have not been properly studied. One of the main reasons is the lack of rich labeled datasets. Specifically, the current datasets are usually only collected from movies and sports, which makes the videos in the dataset far different from actual fight actions. To overcome this drawback, in this paper, we propose a new challenge dataset named TNUE-Fight Detection. In which, our proposed dataset is collected from many real-life fights from videos uploaded to social networks. Furthermore, the TNUE-Fight Detection dataset not only provides labels for each video but also provides bounding boxes for fighting and non-fighting objects, which help to solve the problem in both cases of classification and detection. The TNUE-Fight Detection dataset is available at https://github.com/vdquang1991/TNUE_FightDetection .

Tài liệu tham khảo

[1] Aktı, Ş., Tataroğlu, G.A., Ekenel, H.K.: Vision-based fight detection from surveillance cameras. In: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2019)

[2] Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: a framework for self-supervised learning of speech representations. Adv. Neural Inf. Process. Syst. 33, 12449–12460 (2020)

[3] Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Computer Analysis of Images and Patterns: 14th International Conference, CAIP 2011, Seville, Spain, August 29–31, 2011, Proceedings, Part II 14, pp. 332–339. Springer (2011)

[4] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

[5] Degardin, B., Proença, H.: Human activity analysis: iterative weak/self-supervised learning frameworks for detecting abnormal events. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2020)

[6] Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

[7] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16 x 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). http://openreview.net/forum?id=YicbFdNTTy

[8] Duc, Q.V., Phung, T., Nguyen, M., Nguyen, B.Y., Nguyen, T.H.: Self-knowledge distillation: an efficient approach for falling detection. In: International Conference on Artificial Intelligence and Big Data in Digital Era, pp. 369–380. Springer (2021)

[9] 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)

[10] Liu, A.T., Li, S.W., Lee, H.Y.: Tera: self-supervised learning of transformer encoder representation for speech. IEEE/ACM Trans. Audio, Speech, Lang. Process. 29, 2351–2366 (2021)

[11] Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

[12] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

[13] Soliman, M.M., Kamal, M.H., Nashed, M.A.E.M., Mostafa, Y.M., Chawky, B.S., Khattab, D.: Violence recognition from videos using deep learning techniques. In: 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 80–85. IEEE (2019)

[14] Tan, H.M., Vu, D.Q., Lee, C.T., Li, Y.H., Wang, J.C.: Selective mutual learning: an efficient approach for single channel speech separation. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3678–3682. IEEE (2022)

[15] Tan, H.M., Vu, D.Q., Wang, J.C.: Selinet: a lightweight model for single channel speech separation. In: ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)

[16] Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

[17] Vu, D.Q., Le, N., Wang, J.C.: Teaching yourself: a self-knowledge distillation approach to action recognition. IEEE Access 9, 105711–105723 (2021)

[18] Vu, D.Q., Le, N.T., Wang, J.C.: (2+ 1) d distilled shufflenet: a lightweight unsupervised distillation network for human action recognition. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 3197–3203. IEEE (2022)

[19] Vu, D.Q., Wang, J.C., et al.: A novel self-knowledge distillation approach with Siamese representation learning for action recognition. In: 2021 International Conference on Visual Communications and Image Processing (VCIP), pp. 1–5. IEEE (2021)

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