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Intelligent Visual Tracking in Unstabilized Videos

Intelligent Visual Tracking in Unstabilized Videos

Kamlesh Verma, Debashis Ghosh, Harsh Saxena, Himanshu Singh, Rajeev Marathe, Avnish Kumar
Copyright: © 2020 |Volume: 9 |Issue: 3 |Pages: 22
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781799806677|DOI: 10.4018/IJNCR.2020070104
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MLA

Verma, Kamlesh, et al. "Intelligent Visual Tracking in Unstabilized Videos." IJNCR vol.9, no.3 2020: pp.54-75. http://doi.org/10.4018/IJNCR.2020070104

APA

Verma, K., Ghosh, D., Saxena, H., Singh, H., Marathe, R., & Kumar, A. (2020). Intelligent Visual Tracking in Unstabilized Videos. International Journal of Natural Computing Research (IJNCR), 9(3), 54-75. http://doi.org/10.4018/IJNCR.2020070104

Chicago

Verma, Kamlesh, et al. "Intelligent Visual Tracking in Unstabilized Videos," International Journal of Natural Computing Research (IJNCR) 9, no.3: 54-75. http://doi.org/10.4018/IJNCR.2020070104

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Abstract

Visual tracking requirement is increasing day by day due to the availability of high-performance digital cameras at low prices. Visual tracking becomes a complex problem when cameras suffer with unwanted and unintentional motion, resulting in motion-blurred unstabilized video. The problem in hand becomes more challenging when the target of interest is to be detected automatically in this unstabilized video. This paper presents a comprehensive single intelligent solution for these problems. The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video keeping intentional motion only using speeded-up robust features (SURF) technique. Motion smear due to unstabilization is also removed, providing sharp stabilized video output with video quality enhancement of up to 20dB. Gabor filter is used innovatively for auto-detection of target of interest in each stabilized frame. Then the target is tracked using SURF method.

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