Abstract
This work analyzed video datasets distinctively characterized by varying properties such as duration, fps, and total frames. These datasets also displayed differences in lighting intensities, image shifting, and rotations. The study utilized the YOLOv4 architecture integrated with the Darknet framework for object detection purposes. This combination efficiently extracted bounding box coordinates, defining the Region of Interest (ROI) of the Primary Display Panel in the plane simulator cockpit. Within this ROI, the research employed the SIFT and FAST algorithms, with the Manhattan distance, to determine corner points. The results revealed the FAST algorithm’s ability to detect more key points quicker than SIFT. On the other hand, the latter one turned out to be slightly better when the average Intersection over Union (IoU) is considered. The study’s outcomes can serve for future analysis of pilots’ behavior.
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References
Kumar, C.K., Rawal, K.: A brief study on object detection and tracking. J. Phys. 2327 (2022)
Redmon, J., Farhadi, A.: YOLOv3: an Incremental Improvement. arXiv (2018)
Bochkovskiy, A., Liao, H.-Y. M., Wang, C.-Y.: YOLOv4: optimal speed and accuracy of object detection. arXiv, vol. 1, no. v1, p. 10934 (2020)
Lu, P., Ding, Y., Wang, C.: Multi-small target detection and tracking based on improved yolo and sift for drones. Int. J. Innov. Comput. Inf. Control 17(1), 205–224 (2021)
Srivastava, S., Divekar, A.V., Anilkumar, C., Naik, I., Kulkarni, V., Pattabiraman, V.: Comparative analysis of deep learning image detection algorithms. J. Big Data 8(1), 1 (2021)
Kuang, H., Chen, L., Gu, F., Chen, J., Chan, L., Yan, H.: Combining region-of-interest extraction and image enhancement for nighttime vehicle detection. IEEE Intell. Syst. 31(3), 57–65 (2016)
Gong, T., et al.: Temporal ROI align for video object recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1442–1450 (2021)
Afrin, N., Lai, W.: Effective interest region estimation model to represent corners for image. Sig. Image Process. 9(6), 29–38 (2018)
Kheim, N.Q.M., Ravindra, G., Carlier, A., Ooi, W.T.: Supporting zoomable video streams with dynamic region-of-interest cropping. In: Second Annual ACM Conference on Multimedia Systems (2011)
Zhou, H., Zhang, Y., Yu, Z.: Image classification based on region of interest detection. Pattern Recogn. Comput. Vision 9813 (2015)
Kumar, A.R., Ravindran, B., Raghunathan, A.: Pack and detect: fast object detection in videos using region-of-interest packing. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, India (2019)
Wang, J., Zhang, W.: A survey of corner detection methods. In: 2nd International Conference on Electrical Engineering and Automation (ICEEA) (2018)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Sasikala, N., Swathipriya, V., Ashwini, M., Preethi, V., Pranavi, A., Ranjith, M.: Feature extraction of real-time image using SIFT algorithm. EJECE, Eur. J. Electr. Eng. Comput. Sci. 4(3) (2020)
Viswanathan, D.G.: Features from accelerated segment test (FAST). In: 10th Workshop on Image Analysis for Multimedia Interactive Services, London (2009)
Maresca, M.E., Petrosino, A.: MATRIOSKA: a multi-level approach to fast tracking by learning. Image Anal. Process. 419–428 (2013)
Tang, L., Ma, S., Ma, X., You, H.: Research on image matching of improved SIFT algorithm based on stability factor and feature descriptor simplification. Appl. Sci. 12(17), 8448 (2022)
Fatima, B., Ghafoor, A., Ali, S.S., Riaz, M.M.: FAST, BRIEF and SIFT based image copy-move forgery. Multimedia Tools Appl. 81(30), 43805–43819 (2022)
Muthukrishnan, R., Ravi, J.: Image type-based assessment of SIFT and FAST algorithms. Int. J. Sig. Process. Image Process. Pattern Recogn. 8(3), 211–216 (2015)
Alhwarin, F., Wang, C., Ristic-Durrant, D., Graser, A.: Improved SIFT-features matching for object recognition. In: BSC International Academic Conference - Visions of Computer Science (2008)
Biadgie, Y., Sohn, K.-A.: Feature detector using adaptive accelerated segment test. In: International Conference on Information Science and Applications (ICISA), Seoul (2014)
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This publication was partially supported by the Department of Applied Informatics, under the statue research project (Rau7, 2023), Silesian University of Technology, Gliwice, Poland.
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Nurhasan, A.A., Kasprowski, P., Harezlak, K., Birawo, B.A. (2024). Object Detection in Movies – Case Study. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_1
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