Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques
*Kamal UpretiCorresponding authorkamalupreti1989@gmail.comDepartment of Computer ScienceDelhi NCR CampusCHRIST UniversityUttar Pradesh, IndiaView full profile → , Shreya Kapoorkapoor.shreya2506@gmail.comDepartment of Computer Science and EngineeringDr. Akhilesh Das Gupta Institute of Professional StudiesShashti Park, New Delhi, IndiaView full profile → , Soumi Ghoshghoshdrsoumi@gmail.comDepartment of Information TechnologyMaharaja Agrasen Institute of TechnologyRohini, Delhi, IndiaView full profile → , Dhyanendra Jaindhyanendra.jain@gmail.comDepartment of Computer Science & Engineering(Artificial Intelligence & Machine Learning)ABES Engineering CollegeGhaziabad, Uttar Pradesh, IndiaView full profile → , Virendra Singh Kushwahvirendra.kushwah@vitbhopal.ac.inSchool of Computer Science & EngineeringBhopal-Indore HighwayVIT Bhopal UniversityBhopal, Madhya Pradesh, IndiaView full profile → , Jyoti Parasharjyoti.parashar123@gmail.comDepartment of Computer Science and EngineeringDr. Akhilesh Das Gupta Institute of Professional StudiesShashti Park, New Delhi, IndiaView full profile → , Rituraj Jainjainrituraj@yahoo.comDepartment of Information TechnologyMarwadi UniversityRajkot, Gujarat, IndiaView full profile → , Tarannum Blochtarannum.bloch@marwadieducation.edu.inDepartment of Information TechnologyMarwadi UniversityRajkot, Gujarat, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 26 Jun 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1999
- Pages:
- 1455–1465
Abstract
Keywords
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References
[1] Soleimanitaleb, Z., & Keyvanrad, M.A. Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics (2022). ArXiv, abs/2201.13066.
[2] Haider, Mohammad & Upreti, Kamal & Nasir, Mohammad & Alam, Mohammad & Sharma, A K. Addressing image and Poisson noise deconvolution problem using deep learning approaches. Computational Intelligence Journal (2022). 10.1111/coin.12510.
[3] Pang, L., et al.: A human-following approach using binocular camera. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1487–1492 (2017).
[4] Chi, W., et al.: A gait recognition method for human following in service robots. IEEE Trans. Syst. Man, Cybern. Syst. 48(9), 1429–1440, (2018).
[5] Iswanto, I.: Visual object tracking based on mean-shift and particle-Kalman filter. Procedia Comput. Science A., Li, B. 116, 587– 595 (2017).
[6] K. Upreti, S. Arora, A. K. Sharma, A. K. Pandey, K. K. Sharma and M. Dayal, “Wave Height Forecasting Over Ocean of Things Based on Machine Learning Techniques: An Application for Ocean Renewable Energy Generation,” in IEEE Journal of Oceanic Engineering, doi: 10.1109/JOE.2023.3314090.
[7] Danelljan, Martin & Bhat, Goutam & Khan, Fahad & Felsberg, Michael. ECO: Efficient Convolution Operators for Tracking (2017). 6931-6939. 10.1109/CVPR.2017.733.
[8] Bertinetto, Luca & Valmadre, Jack & Golodetz, Stuart & Miksik, Ondrej & Torr, Philip. Staple: Complementary Learners for Real-Time Tracking (2016). 10.1109/CVPR.2016.156.
[9] Kristan, M. et al. The Visual Object Tracking VOT2016 Challenge Results. In: Hua, G., Jégou, H. (eds) Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science(), vol 9914. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_54
[10] M. Danelljan, A. Robinson, F.S. Khan, M. Felsberg, Beyond correlation filters: Learning continuous convolution operators for visual tracking, in: Proc. 2016 European Conf. Computer Vision, pp. 472–488 (2016).
[11] H. Nam and B. Han, Learning multi-domain convolutional neural networks for visual tracking, in: Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, pp. 4293–4302 (2016).
[12] J. Valmadre, L. Bertinetto, J.F. Henriques, A. Vedaldi, and P.H.S. Torr, End-toend representation learning for correlation filter based tracking, in: Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition, pp. 5000– 5008 (2017).
[13] L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi, P.H.S. Torr, Fully convolutional siamese networks for object tracking, in: Proc. 2016 European Conf. Computer Vision, pp. 850–865 (2016).
[14] Arora, R., Agrawal, A., Arora, R., Poonia, R. C., & Madaan, V. Prediction and forecasting of COVID-19 outbreak using regression and ARIMA models. Journal of Interdisciplinary Mathematics, 24(1), 227–243 (2021).
