• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 33, Pages: 2670-2680

Original Article

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Received Date:07 July 2023, Accepted Date:29 July 2023, Published Date:08 September 2023


Objective: The objective of this work is to detect video shot boundary which is an important phase in the domain of video processing. The applications such as indexing, retrieval and browsing of video are majorly depends on accurate detection of shot boundary so the detection is highly necessary in the area of video processing. Methods: Many researchers have contributed to the field of Shot Boundary Detection (SBD) to improve video processing throughout the previous decade. Some of the techniques have detected abrupt as well as gradual transitions with remarkable performance metrics values, but they compromised with the execution time and operating speed. Some of the techniques proposed and implemented the simpler approaches, but failed to detect the gradual transitions. Additionally, the effects like illuminations and camera motion lead to produce false results. Thus, taking these aspects into consideration, the novel approach for SBD has to be implemented. Findings: In this paper, fusion of transform and filtering at an early stage has been applied for reducing the false hits that occurs due to illumination noise. The fusion of transform with appropriate classifier enhances the detection performance. Another deep learning based technique with Rider bald eagle search optimization given the solution for the SBD. Novelty and applications: The proposed two approaches give the detection of abrupt and gradual transitions. It has been noticed that the said approaches found effective against the illuminations. To evaluate the performance it uses precision, recall and F1 score as performance metrics. The experiments are performed on latest TRECVID datasets and few open source video datasets.

Keywords: Shot Boundary Detection; Walsh Hadamard Transform (WHT); Dual Tree Complex Wavelet Transform; Deep Belief Network; Deep Convolutional Neural Network


  1. Hato E, Abdulmunem ME. Fast Algorithm for Video Shot Boundary Detection Using SURF features. 2019 2nd Scientific Conference of Computer Sciences (SCCS). 2019;p. 81–86. Available from: https://doi.org/10.1109/SCCS.2019.8852603
  2. Wu L, Zhang S, Jian M, Lu Z, Wang D. Two Stage Shot Boundary Detection via Feature Fusion and Spatial-Temporal Convolutional Neural Networks. IEEE Access. 2019;7:77268–77276. Available from: https://doi.org/10.1109/ACCESS.2019.2922038
  3. Chakraborty S, Thounaojam DM. SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect. Multimedia Tools and Applications . 2021;80(2):3071–3087. Available from: https://doi.org/10.1007/s11042-020-09683-y
  4. Rashmi BS, Nagendraswamy HS. Video shot boundary detection using block based cumulative approach. Multimedia Tools and Applications. 2021;80(1):641–664. Available from: https://doi.org/10.1007/s11042-020-09697-6
  5. Bhaumik H, Bhattacharyya S, Chakraborty S. A vague set approach for identifying shot transition in videos using multiple feature amalgamation. Applied Soft Computing. 2019;75:633–651. Available from: https://doi.org/10.1016/j.asoc.2018.10.053
  6. Dhiman S, Chawla R, Gupta S. A novel video shot boundary detection framework employing DCT and pattern matching. Multimedia Tools and Applications. 2019;78(24):34707–34723. Available from: https://doi.org/10.1007/s11042-019-08170-3
  7. Su N, Zhang J, Zhang Y, Zhang G. Unsupervised Clustering Based Real-time Shot Boundary Detection for Live Broadcasting. 2019 IEEE 5th International Conference on Computer and Communications (ICCC). 2019;p. 135–140. Available from: https://doi.org/10.1109/ICCC47050.2019.9064356
  8. Kar T, Kanungo P. Motion and illumination defiant cut detection based on Weber features. IET Image Processing. 2018;12(10):1903–1912. Available from: https://doi.org/10.1049/iet-ipr.2017.1237
  9. Shen RKK, Lin YNN, Juang TTYTY, Shen VRL, Lim SY. Automatic Detection of Video Shot Boundary in Social Media Using a Hybrid Approach of HLFPN and Keypoint Matching. IEEE Transactions on Computational Social Systems. 2018;5(1):210–219. Available from: https://doi.org/10.1109/TCSS.2017.2780882
  10. Mishra R. Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection. Concurrency and Computation: Practice and Experience. 2022;34(25):1–18. Available from: https://doi.org/10.1002/cpe.7256
  11. Chavate S, Mishra R. An Efficient Approach for Shot Boundary Detection in Presence of Illumination Effects using Fusion of Transforms. International Journal of Engineering Trends and Technology. 2022;70(4):418–432. Available from: https://ijettjournal.org/assets/Volume-70/Issue-4/IJETT-V70I4P236.pdf
  12. Ashok PKP, A JD. Deep Learning Approach for Video Shot Boundary Detection. International Journal of Emerging Technologies and Innovative Research . 2020;7(6):411–417. Available from: https://www.jetir.org/view?paper=JETIR2006058
  13. Chavate S, Mishra R, Yadav P. A Comparative Analysis of Video Shot Boundary Detection using Different Approaches. 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). 2021;p. 524–530. Available from: https://ieeexplore.ieee.org/document/9676246
  14. Chakraborty D, Chiracharit W, Chamnongthai K, Charoenpong T. Wipe Scene Change Detection in Object-Camera Motion Based on Linear Regression and an Inflated Spatial-Motion Neural Network. IEEE Access. 2023;11:33080–33099. Available from: https://ieeexplore.ieee.org/document/10086505
  15. Idan ZN, Abdulhussain SH, Mahmmod BM, Al-Utaibi KA, Al-Hadad SAR, Sait SM. Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine. IEEE Access. 2021;9:106412–106427. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9496657
  16. Chavate S, Mishra R. Efficient Detection of Abrupt Transitions Using Statistical Methods. ECS Transactions. 2022;107(1):6541–6552. Available from: https://iopscience.iop.org/article/10.1149/10701.6541ecst/pdf


© 2023 Chavate et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.