Intelligent Visual Tracking in Unstabilized Videos

Intelligent Visual Tracking in Unstabilized Videos

Kamlesh Verma (IRDE, DRDO, Ministry of Defence, India & Indian Institute of Technology, Roorkee, India), Debashis Ghosh (Indian Institute of Technology, Roorkee, India), Harsh Saxena (MSIT, GGSIP University, Delhi, India), Himanshu Singh (IRDE, DRDO, Ministry of Defence, India), Rajeev Marathe (IRDE, DRDO, Ministry of Defence, India) and Avnish Kumar (IRDE, DRDO, Ministry of Defence, India)
Copyright: © 2020 |Pages: 22
DOI: 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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The algorithm proposed in the paper focuses on two major problems i.e. digital video stabilization and visual tracking. To the best of our knowledge, there is no work published for visual tracking in an unstabilized video. We have attempted this complex problem first time and solutions were provided for target detection and tracking in an unstabilized visual sequences. In (Verma et al., 2018) algorithm, we have developed an algorithm which stabilizes the input video using target templates and affine transformation. Once the frame is stabilized, L-1 minimization is computed under the frame work of sparse solution matrix. The energy of coefficients is used to solve the occlusion problem. In our next work (Verma et al., 2019), we proposed a combined algorithm to simultaneously stabilize the frames and track the target in unstabilized video after stabilizing using a single frame work of Speeded Up Robust Features (SURF) method. Now we have added Gabor Filter based auto-detection of target method in the proposed method for robustness of above algorithm. A dedicated hardware design was presented (Verma, Kamlesh, et al., 2019) for electronic image stabilization and visual tracking. A novel approach was developed in (Verma, Kamlesh, et al., 2018) for video stabilization using the detection of target. Camera intentional motion was preserved while rejecting unintentional camera motion in the algorithm developed in (Saxena, Harsh, et al., 2019) for video stabilization. Since besides these works, no other work has been published till date, we present the related work under two separate headings of video stabilization and visual tracking as below.

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