Avnish Kumar
Defence Research and Development Organisation
8 Papers
13 Citations
Avnish Kumar is an academic researcher from Defence Research and Development Organisation. The author has contributed to research in topics: Motion vector & Image stabilization. The author has an hindex of 2, co-authored 8 publications.
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Papers
Visual tracking in unstabilized real time videos using SURF
TL;DR: An algorithm is developed and presented to auto-stabilize the video, auto detect the target of interest followed by tracking, which has been experimentally verified and outperforms with bench marked visual tracking algorithms.
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Fast and robust real time digital video stabilization with smear removal using integral projection curve warping technique
Kamlesh Verma,Avnish Kumar,Sumana Gupta,K. S. Venkatesh +3 more
- 01 Feb 2014
TL;DR: The fractional i.e. subpixel motion compensation for videos having very small amount of disturbances enables accurate localization of targets and proper calibration of critical instruments having drift phenomena due to electronics hardware, specially for defence applications and consumer electronics having very less jitters.
8
Digital Video Stabilization with Preserved Intentional Camera Motion and Smear Removal
Harsh Saxena,Kamlesh Verma,Debashis Ghosh,Avnish Kumar +3 more
- 01 Jul 2019
TL;DR: A computer vision method is presented to segregate unintentional and intentional motion (both translation and rotational) and promising video results have been obtained for digital video stabilization keeping intentional motion and removing motion smear.
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Video Stabilization Through Target Detection
Kamlesh Vrma,Debashis Ghosh,Avnish Kumar +2 more
- 01 Nov 2018
TL;DR: A novel algorithm is developed which auto-detects the target and uses these parameters to stabilize the video itself and this developed algorithm calculates the local and global motion vectors simultaneously.
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Intelligent Visual Tracking in Unstabilized Videos
TL;DR: The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video while keeping intentional motion only using speeded-up robust features (SURF) technique.
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