Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1577997 Computer Software Software Engineering (Other) Bilgisayar Yazılımı Yazılım Mühendisliği (Diğer) Improving Face Detection Performance of Compressed MPEG Videos by Using Frame-Independent Scene Change Detection Method https://orcid.org/0000-0002-2901-2342 Özdem Mehmet Türk Telekom 09 30 2025 13 3 376 381 11 02 2024 11 28 2024 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

With the spread of computer vision applications, the performance of such applications also became prominent, especially when real-time and near real-time use cases are considered. If not all, many object detection algorithms follow a frame-based search approach, where all frames of the MPEG stream are analyzed sequentially. This drastically increases the computation time and the hardware requirements for such systems. This paper proposes employing a new scene-change detection method to improve object and face detection performance by eliminating the need to analyze every video frame. The method provides a frameindependent approach and does not require decoding and reencoding of MPEG video. The paper also reports the performance test outcomes to exhibit the proposed approach’s value. The findings show that a scene-change detection method enhances efficiency and decreases computational demands. Focusing on frames that show scene changes shows notable advancements in object detection performance.

 Face detection Object detection Frame-by-frame analysis [1] E. Hjelmas and B. Low, “Face detection: A survey,” ˚ Computer Vision and Image Understanding, vol. 83, no. 3, September 2001. [2] L. Jiao, R. Zhang, F. Liu, S. Yang, B. Hou, L. Li, and X. Tang, “New generation deep learning for video object detection: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 8, August 2022. [3] L. Baraldi, C. Grana, and R. Cucchiara, “Measuring scene detection performance,” in Pattern Recognition and Image Analysis: Proceedings of 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 2015. [4] I. Sethi and N. Patel, “A statistical approach to scene change detection,” in Proceedings of SPIE - The International Society for Optical Engineering, June 1998. [5] C.-L. Huang and B.-Y. Liao, “A robust scene-change detection method for video segmentation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 12, December 2001. [6] B. Shahraray, “Scene change detection and content-based sampling of video sequences,” in Proceedings Volume 2419, Digital Video Compression: Algorithms and Technologies, April 1995. [7] R. Das¸, B. Polat, and G. Tuna, “Recognizing and tracking objects in images and videos with deep learning,” Fırat University, Muhendislik ¨ Bilimleri Dergisi, vol. 31, no. 2, pp. 571–581, 2019. [8] Bitmovin, “Video developer report 2018,” September 2019. [9] A. Huszak and S. Imre, “Analysing gop structure and packet loss effects on error propagation in mpeg-4 video streams,” in Proceedings of 4th International Symposium on Communications Control and Signal Processing (ISCCSP), March 2010. [10] B. Zatt, M. Porto, J. Scharcanski, and S. Bampi, “Gop structure adaptive to the video content for efficient h.264/avc encoding,” in Proceedings of IEEE International Conference on Image Processing, September 2010. [11] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, April 2004. [12] Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Transactions on Image Processing, May 2011.