Camera Motion Detection using Video Mosaicing

Naito, Masaki; Matsumoto, Kazunori; Hoashi, Keiichiro; Sugaya, Fumiaki

doi:10.1109/icme.2006.262887

Cited by 3 publications

(1 citation statement)

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“…Different camera motions (such as zoom, rotation, pan, tilt, object motion and stationary) are recognized by comparing the computed mean values and standard deviations with the prior known patterns. Naito et al [8] propose a camera motion detection method using a background image generated by video mosaicking based on the correlation between feature points on a frame pair. To detect the camera motion, the position of frames on the background image is converted to feature parameters so as the coordinate of the center of each quadrangle camera frame and the distance between the center and vertex of the quadrangle frame respectively.…”

Section: Related Workmentioning

confidence: 99%

A robust method for camera motion estimation in movies based on optical flow

Nguyen

Laurendeau

Branzan-Albu

2010

IJISTA

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Camera motion estimation plays an important role in digital video analysis algorithms such as video indexing and retrieval or automatic movie analysis. Several algorithms have been proposed to solve this problem in MPEG videos. This paper presents an optical flow based approach for the camera motion estimation in all kinds of digital video formats, especially in movies. It compares the motion vector fields (MVFs) with six predefined templates to determine the type of motion. The MVFs are generated by using high-accuracy optical flow computation. The advantage of the method lies in its robustness to noisy environments such as false motion vectors and object motions. Comprehensive experiments with video clips extracted from well-known feature movies demonstrate the performance of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A robust method for camera motion estimation in movies based on optical flow

Nguyen

Laurendeau

Branzan-Albu

2010

IJISTA

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Automatic 3D-display-friendly scene extraction from video sequences and optimal focusing distance identification

Chlubna,

Milet,

Zemčík

2024

Multimed Tools Appl

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This paper proposes a method for an automatic detection of 3D-display-friendly scenes from video sequences. Manual selection of such scenes by a human user would be extremely time consuming and would require additional evaluation of the result on 3D display. The input videos can be intentionally captured or taken from other sources, such as films. First, the input video is analyzed and the camera trajectory is estimated. The optimal frame sequence that follows defined rules, based on optical attributes of the display, is then extracted. This ensures the best visual quality and viewing comfort. The following identification of a correct focusing distance is an important step to produce a sharp and artifact-free result on a 3D display. Two novel and equally efficient focus metrics for 3D displays are proposed and evaluated. Further scene enhancements are proposed to correct the unsuitably captured video. Multiple image analysis approaches used in the proposal are compared in terms of both quality and time performance. The proposal is experimentally evaluated on a state-of-the-art 3D display by Looking Glass Factory and is suitable even for other multi-view devices. The problem of optimal scene detection, which includes the input frames extraction, resampling, and focusing, was not addressed in any previous research. Separate stages of the proposal were compared with existing methods, but the results show that the proposed scheme is optimal and cannot be replaced by other state-of-the-art approaches.

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