Scopus; WoS

A Lightweight Transformer-Based Model for Fight Recognition

Năm XB 2024 Tạp chí / Hội thảo Communications in Computer and Information Science Volume 2310 CCIS DOI / Link https://doi.org/10.1007/978-981-96-0437-1_25 ↗

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Tóm tắt

Recognizing violent actions is critical for ensuring timely detection and prevention. While numerous machine learning models, particularly deep learning approaches, have been developed to address this issue, many focus solely on performance enhancement, overlooking...

Tài liệu tham khảo

[1] Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 332–339. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23678-5_39

[2] Carneiro, S.A., da Silva, G.P., Guimaraes, S.J.F., Pedrini, H.: Fight detection in video sequences based on multi-stream convolutional neural networks. In: SIBGRAPI, pp. 8–15. IEEE (2019)

[3] Chen, Y., et al.: Mobile-former: Bridging mobilenet and transformer. In: CVPR, pp. 5270–5279 (2022)

[4] Cheng, M., Cai, K., Li, M.: RWF-2000: an open large scale video database for violence detection. In: ICPR, pp. 4183–4190 (2021)

[5] Cheng, W.C., Mai, T.H., Lin, H.T.: From SMOTE to mixup for deep imbalanced classification. In: Lee, C.Y., Lin, C.L., Chang, H.T. (eds.) TAAI 2023. CCIS, vol. 2074, pp. 75–96. Springer, Cham (2023)

[6] Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: RandAugment: practical automated data augmentation with a reduced search space. In: CVPR, pp. 702–703 (2020)

[7] Dang, A., Linh, H.M., Vu, D.Q.: Multi-scale aggregation network for speech emotion recognition. In: Hà, M.H., Zhu, X., Thai, M.T. (eds.) CSoNet. LNCS, vol. 14479, pp. 63–73. Springer, Singapore (2023). https://doi.org/10.1007/978-981-97-0669-3_6

[8] Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: ICCV, pp. 6202–6211 (2019)

[9] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

[10] Howard, A., et al.: Searching for mobilenetv3. In: ICCV, pp. 1314–1324 (2019)

[11] Kang, M.S., Park, R.H., Park, H.M.: Efficient spatio-temporal modeling methods for real-time violence recognition. IEEE Access 9, 76270–76285 (2021)

[12] Khan, M., El Saddik, A., Gueaieb, W., De Masi, G., Karray, F.: VD-Net: an edge vision-based surveillance system for violence detection. IEEE Access 12 (2024)

[13] Nga, C.H., Vu, D.Q., Luong, H.H., Huang, C.L., Wang, J.C.: Cyclic transfer learning for mandarin-english code-switching speech recognition. IEEE Signal Process. Lett. (2023)

[14] Phung, T., Nguyen, V.T., Ma, T.H.T., Duc, Q.V.: A (2+1) d attention convolutional neural network for video prediction. In: Dang, N.H.T., Zhang, YD., Tavares, J.M.R.S., Chen, BH. (eds.) ICABDE 2021, vol. 124, pp. 395–406. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-97610-1_31

[15] Serrano Gracia, I., Deniz Suarez, O., Bueno Garcia, G., Kim, T.K.: Fast fight detection. PloS one 10(4), e0120448 (2015)

[16] 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: ICICIS, pp. 80–85. IEEE (2019)

[17] Sudhakaran, S., Lanz, O.: Learning to detect violent videos using convolutional long short-term memory. In: AVSS, pp. 1–6. IEEE (2017)

[18] Tan, H.M., Vu, D.Q., Thi, D.N., Thu, T.P.T.: Voice separation using multi learning on squash-norm embedding matrix and mask. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, V.N. (eds.) ICTA 2023. LNNS, vol. 848, pp. 327–333. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-50818-9_36

[19] Tan, H.M., Vu, D.Q., Wang, J.C.: Selinet: a lightweight model for single channel speech separation. In: ICASSP, pp. 1–5. IEEE (2023)

[20] Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105–6114. PMLR (2019)

[21] Tran, D., et al.: A closer look at spatiotemporal convolutions for action recognition. In: CVPR, pp. 6450–6459 (2018)

[22] Tran, H.N., Jeon, J.W.: Robust speed controller using dual adaptive sliding mode control (DA-SMC) method for PMSM drives. IEEE Access 11, 63261–63270 (2023)

[23] Vaswani, A., et al.: Attention is all you need. NIPS 30 (2017)

[24] Vu, D.Q., Nguyen, T.H., Nguyen, M., Nguyen, B.Y., Phung, T.N., Thu, T.P.T.: TNUE-Fight detection: a new challenge benchmark for fighting recognition. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, V.N. (eds.) ICTA 2023. LNNS, vol. 848, pp. 308–314. Springer, Cham (2023)

[25] Vu, D.Q., Phung, T.T., Wang, J.C., Mai, S.T.: LCSL: long-tailed classification via self-labeling. IEEE TCSVT (2024)

[26] Vu, D.Q., Thu, T.P.T.: Simultaneous context and motion learning in video prediction. SIViP 17(8), 3933–3942 (2023)

[27] Wang, J., Zhao, D., Li, H., Wang, D.: Lightweight violence detection model based on 2D CNN with bi-directional motion attention. Appl. Sci. 14(11) (2024)