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              <text>Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques</text>
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              <text>Bounding box; Deep learning convolutional network; Multiple object tracking; Single object tracking; Visual object tracking</text>
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              <text>Visual tracking is crucial to the study of object recognition and has been utilized in a variety of realistic settings, such as robotics, traffic monitoring, self-driving automobiles, forensics, and more. This research concentrates on techniques for counting the total number of individuals entering or exiting a space under the watchful eye of a camera. The techniques described here can detect the number of persons in a scene, both for a single individual and for many passers in front of the camera. With the aid of surveillance that use the centroid concept, an effective solution has been devised for monitoring. Secondly, in this study, object tracking methods utilising deep learning are also reviewed and analysed. This study also compares the effectiveness of various algorithms on the LaSOT, VOT2015, VOT2016, VOT2017 and OTB2015 tests.  2024, Taru Publications. All rights reserved.</text>
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              <text>Upreti K.; Kapoor S.; Ghosh S.; Jain D.; Kushwah V.S.; Parashar J.; Jain R.; Bloch T.</text>
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              <text>Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 4, pp. 1455-1465.</text>
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              <text>Taru Publications</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.47974/JDMSC-1999" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.47974/JDMSC-1999&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISSN: 9720529</text>
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              <text>Upreti K., Department of Computer Science, CHRIST University, Delhi NCR Campus Uttar Pradesh, India; Kapoor S., Department of Computer Science Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, India; Ghosh S., Department of Information Technology, Maharaja Agrasen Institute of Technology, Rohini Delhi, India; Jain D., Department of Computer Science &amp;amp; Engineering (Artificial Intelligence &amp;amp; Machine Learning), ABES Engineering College, Uttar Pradesh, Ghaziabad, India; Kushwah V.S., School of Computer Science &amp;amp; Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, India; Parashar J., Department of Computer Science Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Bloch T., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India</text>
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