Abstract
This research displays an ongoing framework to identification of various occluding questions in element scenes. Article identification is a workstation engineering that bargains for identifying examples for same questions of a part (likely similar as peoples, vehicles, or buildings) for advanced pictures Also features. In the primary objective from claiming impediment identification from feature In utilizing Gaussian mixture model (GMM) strategy which will be foundation demonstrating will be should yield reference model What’s more this reference model is utilized within foundation subtraction done each feature grouping may be compared against those reference model will focus time permits variety. Then impediment identification In light of Questions pixels qualities.
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Gabriel PF, Verly JG, Piater JH, Genon A (2003) The state of the art in multiple object tracking under occlusion in video sequences. In: Proceedings of advanced concepts for intelligent vision systems, pp 166–173
Parekh HS, Thakore DG, Jaliya UK (2014) A survey on object detection and tracking methods. Int J Innov Res Comput Commun Eng (IJIRCCE) 2
Rakibe RS, Patil BD (2013) Background subtraction algorithm based human motion detection. Int J Sci Res Publ (2013)
Chauhan AK, Krishan P (2013) Moving object tracking using gaussian mixture model and optical flow. Int J Adv Res Comput Sci Softw Eng
Santosh DHH, Venkatesh P, Rao LN, Kumar NA (2013) Tracking multiple moving objects using Gauusian mixture model. Int J Soft Comput Eng (IJSCE) 3(2)
Rao GM, Satyanarayana C (2014) Object tracking system using approximate median filter, Kalman filter and dynamic template matching
Khan S, Shah M (2000) Tracking people in presence of occlusion. In: Asian conference on computer vision
Xiao J, Cheng H, Sawhney H, Rao C, Isnardi M (2006) Bilateral filtering-based optical flow estimation with occlusion detection. In: Proceedings of computer vision—ECCV 2006. Springer, Berlin, Heidelberg, pp 211–224
Piater JH, Crowley JL (2001) Multi-modal tracking of interacting targets using Gaussian approximations. In: Second IEEE international workshop on performance evaluation of tracking and surveillance, vol 14, p 58
Rosales R, Sclaroff S (1998) Improved tracking of multiple humans with trajectory prediction and occlusion modeling. In: IEEE CVPR workshop on the interpretation of visual motion
Yang T, Pan Q, Li J, Li SZ (2005) Real-time multiple objects tracking with occlusion handling in dynamic scenes. In: IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, vol 1, pp 970–975. IEEE
Chang, T-H, Gong S, Ong E-J (2000) Tracking multiple people under occlusion using multiple cameras. In: Proceedings of BMVC, pp 1–10
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Gaur, S., Degadwala, S., Mahajan, A. (2019). Multiple Objects Tracking Under Occlusion Detection in Video Sequences. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_23
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DOI: https://doi.org/10.1007/978-981-13-2285-3_23
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