Abstract
The difficult tasks of shadow identification and removal span a wide range of contexts, including computer vision, indoor and outdoor scenarios, and reconnaissance framework. An activity observation framework's performance may suffer if a shadow is incorrectly identified as a foreground object. Although many algorithms are available for removing shadow from images there are some limitations. The goal of this article is to provide a brief overview of the various algorithms used in shadow detection, shadow removal, and ghost elimination, along with their benefits and drawbacks. The article also offered a novel background subtraction-based technique using morphological operation for eliminating shadows from video frames that successfully identifies the shadow region in a frame and eliminates it from the frame. Furthermore, a comparison is made between the proposed method and the existing approaches, and the experimental findings demonstrate its robustness. The experimental outcomes exhibit that the proposed approach shows its effectiveness in terms of an accuracy of 81.37%, precision of 0.81, recall of 0.84, and f1 score of 0.83 compared to other existing approaches.
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References
Tom AJ, George SN (2020) Simultaneous reconstruction and moving object detection from compressive sampled surveillance videos. IEEE Trans Image Process 29:7590–7602
Chen Y, Wang J, Zhu B, Tang M, Lu H (2019) Pixelwise deep sequence learning for moving object detection. IEEE Trans Circuits Syst Video Technol 29(9):2567–2579
Eltantawy A, Shehata MS (2019) An accelerated sequential PCP-based method for ground-moving objects detection from aerial videos. IEEE Trans Image Process 28(12):5991–6006
Sajid H, Cheung S-CS (2017) Universal multimode background subtraction. IEEE Trans Image Process 26(7):3249–3260
Yun K, Lim J, Choi JY (2017) Scene conditional background update for moving object detection in a moving camera. Pattern Recogn Lett 88(1):57–63
Zheng A, Zhang L, Zhang W, Li C, Tang J, Luo B (2017) Local-to-global background modeling for moving object detection from non-static cameras. Multimedia Tools Appl 76(8):11003–11019
Pal T (2021) Improved background subtraction technique for detecting moving objects. Rec Adv Comp Sci Commun 14(9)
Minaeian S, Liu J, Son YJ (2018) Effective and efficient detection of moving targets from a UAV’s camera. IEEE Trans Intell Transp Syst 19(2):497–506
Wibowo SA, Lee H, Kim EK, Kim S (2018) Collaborative learning based on convolutional features and correlation filter for visual tracking. Int J Control Autom Syst 16(1):335–349
Barnich O, Droogenbroeck MV (2011) Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724
Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition (ICPR), vol 2
Kim Y, Bae T, Ahn S (2020) Background subtraction with shadow removal using hue and texture model for moving object detection. In: International conference on electronics, information, and communication (ICEIC). Barcelona, Spain, pp 1–2
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Pal, T. (2023). Morphological Operation-Based Background Subtraction Method for Shadow Removal in Outdoor Video Sequences. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_32
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DOI: https://doi.org/10.1007/978-981-99-4284-8_32
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