Deep Learning Based Sports Video Classification Research

[1] Kang, D., Bailis, P., & Zaharia, M. (2018). BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. Search in Google Scholar

[2] Muhammad, K., Obaidat, M. S., Hussain, T., et al. (2021). Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions. ACM Computing Surveys, 54(3), 1-33. Search in Google Scholar

[3] Martin, P. E. (2021). Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark. Search in Google Scholar

[4] Yang, H., & Meinel, C. (2014). Content based lecture video retrieval using speech and video text information. IEEE Transactions on Learning Technologies, 7(2), 142-154. Search in Google Scholar

[5] Kennedy-Metz, L. R., Mascagni, P., et al. (2021). Computer vision in the operating room: Opportunities and caveats. IEEE Transactions on Medical Robotics and Bionics, 3(1), 2-10. Search in Google Scholar

[6] Liu, S., Cheng, Li, F., et al. (2015). TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1696-1709. Search in Google Scholar

[7] Chen, Y., He, W., Hua, Y., et al. (2016). Compound eyes: Near-duplicate detection in large scale online video systems in the cloud. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. Search in Google Scholar

[8] Wang, Y., Chen, W. T., Wu, H., et al. (2016). A cloud-based large-scale distributed video analysis system. In: 2016 IEEE International Conference on Image Processing. Search in Google Scholar

[9] Elsharkawy, Z. F., Abdelwahab, S. A. S., Abd El-Samie, F. E., Dessouky, M., & Elaraby, S. (2019). New and efficient blind detection algorithm for digital image forgery using homomorphic image processing. Multimedia Tools and Applications, 78(15). Search in Google Scholar

[10] Cc, A., Gl, B., Rw, A., et al. (2021). Graph-Based Structural Difference Analysis for Video Summarization. Information Sciences. Search in Google Scholar

[11] Zhi-Yuan, B. I., & Department PE. (2016). A Case Study of Basketball Technical and Tactical Statistical Analysis Based on Simi Scout Method. Zhejiang Sport Science, 38(02), 123-128. Search in Google Scholar

[12] Radenkovi, L., Miloevi, M., & Nei, L. (2020). Physics of powerlifting: bar force profile during deadlift using video analysis and accelerometers. European Journal of Physics, 42(1), 015803 (15pp). Search in Google Scholar

[13] Sundermeyer, M., Ney, H., & Schlüter, R. (2015). From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language. Search in Google Scholar

[14] An, T., Yu, H., Yang, C., et al. (2020). Black tea withering moisture detection method based on convolution neural network confidence. Journal of Food Process Engineering, 43(6). Search in Google Scholar

[15] Luo, T., Zhu, Y., Wu, W., et al. (2017). Online makespan minimization in MapReduce-like systems with complex reduce tasks. Optimization Letters. Search in Google Scholar

[16] Du, D. B., & Rajesh, V. (2022). Optimized threshold-based convolutional neural network for plant leaf classification: a challenge towards untrained data. Journal of Combinatorial Optimization, 43. Search in Google Scholar

[17] Wu, Y., Lim, J., & Yang, M. H. (2013). Online Object Tracking: A Benchmark. Computer Vision & Pattern Recognition. IEEE. Search in Google Scholar

[18] Yin, X., & Liu, G. (2019). An Effective Appearance Model Update Strategy in Object Tracking. IET Computer Vision, 13(6). Search in Google Scholar