Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

Dongsheng Chen; Zhen Ni

doi:10.4108/eetsis.3955

Authors

Dongsheng Chen Guangxi College for Preschool Education
Zhen Ni Nanning Normal University

DOI:

https://doi.org/10.4108/eetsis.3955

Keywords:

Deep learning, Particle filter, Sports video, Categorize, Edge detection, Key frame, Encoding mode

Abstract

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

References

Yuan, Y., Lu, Z., Yang, Z., Jian, M., Wu, L., Li, Z., & Liu, X. Key frame extraction based on global motion statistics for team-sport videos. Multimedia Systems,2022; 28(2):387-401.

Shingrakhia, H., & Patel, H. Cricket Video Highlight Generation Methods: A Review. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2022; 21(2):1-22.

Soundararajan, K. Sports highlight recognition and event detection using rule inference system. Concurrent Engineering, 2022; 30(2):206-213.

Fu, Q., Ma, L., Li, C., Li, Z., Zhu, Z., & Lin, Z. Detection method of sports scene conversion for MPEG compressed video based on fuzzy logic. Journal of Intelligent & Fuzzy Systems, 2021; 40(2):3107-3115.

Guntuboina, C., Porwal, A., Jain, P., & Shingrakhia, H. Deep learning based automated sports video summarization using YOLO. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2021; 20(1):99-116.

Chakraborty, S., Thounaojam, D. M., & Sinha, N. A shot boundary detection technique based on visual colour information. Multimedia Tools and Applications, 2021;80(3):4007-4022.

Chakraborty, S., Singh, A., & Thounaojam, D. M. A novel bifold-stage shot boundary detection algorithm: invariant to motion and illumination. The Visual Computer, 2022; 38(2):445-456.

Chakraborty, S., & Thounaojam, D. M. SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect. Multimedia Tools and Applications, 2021; 80(2):3071-3087.

Cao, Y., & Mao, H. High-dimensional multi-objective optimization strategy based on directional search in decision space and sports training data simulation. Alexandria Engineering Journal, 2022; 61(1):159-173.

Tiwari, V., & Bhatnagar, C. A survey of recent work on video summarization: approaches and techniques. Multimedia Tools and Applications, 2021; 80(18):27187-27221.

Raval, K. R., & Goyani, M. M. A survey on event detection based video summarization for cricket. Multimedia Tools and Applications, 2022; 81(20):29253-29281.

Shingrakhia, H., & Patel, H. SGRNN-AM and HRF-DBN: a hybrid machine learning model for cricket video summarization. The Visual Computer, 2022; 38(7):2285-2301.

Liu, Y., Dhakal, S., & Hao, B. Multimedia image and video retrieval based on an improved HMM. Multimedia Systems, 2022; 28(6):2093-2103.

Singh, A., Singh, T. D., & Bandyopadhyay, S. V2t: video to text framework using a novel automatic shot boundary detection algorithm. Multimedia Tools and Applications, 2022; 81(13):17989-18009.

Adly, A. S., Hegazy, I., Elarif, T., & Abdelwahab, M. S. Indexed Dataset from YouTube for a Content-Based Video Search Engine. International Journal of Intelligent Computing and Information Sciences, 2021;21(1):196-215.

Chakraborty S, Thounaojam D M. A novel shot boundary detection system using hybrid optimization technique. Applied Intelligence, 2019, 49(9): 3207-3220.

Zhang B. Moving target detection and trackingbased on camshift algorithm and kalmanfilter in sport video. International Journal of Performability Engineering, 2019, 15(1): 288.