Abstract
Video observation is fundamental for guaranteeing public well-being and security in different environments such as air terminals, train stations, retail outlets, and local residential locations. Existing video examination techniques face significant restrictions and difficulties, like low precision, high computational intricacy, and restricted flexibility to evolving environmental conditions. To address these difficulties, this paper proposes an original way to deal with improving video examination for distant observation by consolidating progressed object location, following and following behavioural algorithms into a unified structure. Faster deep learning object detection Algorithms like R-CNN, YOLO, and SSD are used here to precisely recognize and restrict objects of interest in surveillance videos. We investigated a few benchmark datasets and contrasted their presentation and cutting-edge techniques. The outcomes show that the proposed approach beats existing precision, strength, and efficiency strategies.
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References
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468. IEEE (2016)
Chen et al.: Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Indus. Informat. (2020). https://doi.org/10.1109/tii.2019.2909473
Conche., Greten Xiao et al.: Towards real-time service from remote sensing: Compression of earth observatory video data via long-term background referencing. Remote Sens. (2018). https://doi.org/10.3390/rs10060876
Gao, Y., Zhang, J., Liu, Y.: A survey of video-based human activity recognition. Multimedia Tools Appl. 78(9), 11407–11433 (2019). https://doi.org/10.1007/s11042-019-08170-4
Jiang et al.: Federated dynamic graph neural networks with secure aggregation for video-based distributed surveillance. ACM Trans. Intell. Syst. Technol. (2022). https://doi.org/10.1145/3501808
Kachhoria, R., Jaiswal, S., Lokhande, M., Rodge, J.: Lane detection and path prediction in autonomous vehicle using deep learning. Intelligent Edge Computing for Cyber Physical Applications, pp. 111–127 (2023)
Khan, M.A., Han, K.: A survey on video-based crowd detection and density estimation. ACM Comput. Surv. 51(4), 1–34 (2018)
Kumar, A., Kaur, M., Kaur, K.: Review of object detection and tracking techniques in video surveillance. J. Ambient. Intell. Humaniz. Comput. 11(6), 2199–2219 (2020). https://doi.org/10.1007/s12652-019-01696-9
Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D.: MOTChallenge 2016: benchmarking multi-object tracking (2017). arXiv preprint arXiv:1703.07402.
Li, X., Hu, W., Hu, Q., Zhang, X.: Real-time object detection in video surveillance: a review. J. Electron. Imaging 29(1), 1–22 (2020). https://doi.org/10.1117/1.JEI.29.1.010901
Li, B., Li, Y., Zhang, D.: A survey of video-based human action recognition. IEEE Trans. Circuits Syst. Video Technol. 31(1), 1–15 (2021). https://doi.org/10.1109/TCSVT.2020.2982304
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., & Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 91–99 (2015)
Ruban, S.R, Jayaseelan, P., Suresh, M., RatnaKandavalli, S.: Effect of textures on machining of carbon steel under dry cutting condition. In: IOP Conference Series: Materials Science and Engineering, vol. 993, no. 1, p. 012143. IOP Publishing (2020)
Wang, L., Wang, Y., Ye, Q.: A survey of pedestrian detection and tracking in video surveillance. J. Vis. Commun. Image Represent. 78, 102885 (2021)
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets (2016). arXiv preprint arXiv:1507.02159.
Zhang, H., Xu, T., Li, H., Wang, X., Huang, X., Huang, C.: Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 352–365 (2019)
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SS oversaw the team's joint efforts, conceptualized the study's central premise, and wrote a significant amount of the article. The experimental design was shaped by KA knowledge of deep learning techniques like R-CNN, YOLO, and SSD. VPV concentrated on the architectural elements of the federated learning system, while RP oversaw data interpretation and analysis. AA oversaw the collection and preparation of data and played a key role in the execution of the algorithm. Last but not least, KA offered valuable suggestions that improved the approach, findings, and general coherence of the work. All of the authors participated in discussions, research, and article evaluation.
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Selvi, S., Aggarwal, K., Pandurangan, R. et al. Enhancing the accuracy of target detection in remote video surveillance analytics through federated learning. Opt Quant Electron 56, 185 (2024). https://doi.org/10.1007/s11082-023-05664-1
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DOI: https://doi.org/10.1007/s11082-023-05664-1