How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field

Kong, Kyeongbo; Shin, Seung‐Jun; Lee, Junggi; Song, Woo‐Jin

doi:10.1109/tcsvt.2018.2882513

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The motion of the background is usually caused by the change in the position of the recording device, which is called global motion [ 8 , 9 ]. The movement of the foreground is the movement of the moving object relative to the recording device, which is a local movement [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Video Global Motion Compensation Based on Affine Inverse Transform Model

Zhang,

Liu,

Xia

2023

Sensors

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Global motion greatly increases the number of false alarms for object detection in video sequences against dynamic backgrounds. Therefore, before detecting the target in the dynamic background, it is necessary to estimate and compensate the global motion to eliminate the influence of the global motion. In this paper, we use the SURF (speeded up robust features) algorithm combined with the MSAC (M-Estimate Sample Consensus) algorithm to process the video. The global motion of a video sequence is estimated according to the feature point matching pairs of adjacent frames of the video sequence and the global motion parameters of the video sequence under the dynamic background. On this basis, we propose an inverse transformation model of affine transformation, which acts on each adjacent frame of the video sequence in turn. The model compensates the global motion, and outputs a video sequence after global motion compensation from a specific view for object detection. Experimental results show that the algorithm proposed in this paper can accurately perform motion compensation on video sequences containing complex global motion, and the compensated video sequences achieve higher peak signal-to-noise ratio and better visual effects.

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Section: Introductionmentioning

confidence: 99%

Video Global Motion Compensation Based on Affine Inverse Transform Model

Zhang,

Liu,

Xia

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the classic computer vision literature, several studies have been conducted for video processing, such as frame interpolation, 1,2 global motion estimation, 3,4 and video frame classification 5 . Among them, video frame classification, which performs classification at the frame level, has increased in importance owing to its use in various applications such as 4D effect video classification 6 and genre classification.…”

Section: Introductionmentioning

confidence: 99%