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Motion compensation

About: Motion compensation is a(n) research topic. Over the lifetime, 21316 publication(s) have been published within this topic receiving 370672 citation(s).

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Open accessProceedings ArticleDOI: 10.1109/ICCV.2013.441
Heng Wang1, Cordelia Schmid1Institutions (1)
01 Dec 2013-
Abstract: Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.

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  • Figure 5. From left to right, example frames from (a) Hollywood2, (b) HMDB51, (c) Olympic Sports and (d) UCF50.
    Figure 5. From left to right, example frames from (a) Hollywood2, (b) HMDB51, (c) Olympic Sports and (d) UCF50.
  • Figure 1. First row: images of two consecutive frames overlaid; second row: optical flow [8] between the two frames; third row: optical flow after removing camera motion; last row: trajectories removed due to camera motion in white.
    Figure 1. First row: images of two consecutive frames overlaid; second row: optical flow [8] between the two frames; third row: optical flow after removing camera motion; last row: trajectories removed due to camera motion in white.
  • Table 1. Comparison of the baseline with our method and two intermediate results using FV encoding. “WarpFlow”: computing motion descriptors (i.e., Trajectory, HOF and MBH) using warped optical flow, while keep all the trajectories; “RmTrack”: removing background trajectories, but computing motion descriptors using the original flow field; “Combined”: removing background trajectories, and computing Trajectory, HOF and MBH with warped optical flow.
    Table 1. Comparison of the baseline with our method and two intermediate results using FV encoding. “WarpFlow”: computing motion descriptors (i.e., Trajectory, HOF and MBH) using warped optical flow, while keep all the trajectories; “RmTrack”: removing background trajectories, but computing motion descriptors using the original flow field; “Combined”: removing background trajectories, and computing Trajectory, HOF and MBH with warped optical flow.
  • Table 2. Comparison of feature encoding with bag of features and Fisher vector. “DTF” stands for the original dense trajectory features [40] with RootSIFT normalization, whereas “ITF” are our improved trajectory features.
    Table 2. Comparison of feature encoding with bag of features and Fisher vector. “DTF” stands for the original dense trajectory features [40] with RootSIFT normalization, whereas “ITF” are our improved trajectory features.
  • Figure 3. Examples of removed trajectories under various camera motions, e.g., pan, zoom, tilt. White trajectories are considered due to camera motion. The red dots are the trajectory positions in the current frame. The last row shows two failure cases. The left one is due to severe motion blur. The right one fits the homography to the moving humans as they dominate the frame.
    Figure 3. Examples of removed trajectories under various camera motions, e.g., pan, zoom, tilt. White trajectories are considered due to camera motion. The red dots are the trajectory positions in the current frame. The last row shows two failure cases. The left one is due to severe motion blur. The right one fits the homography to the moving humans as they dominate the frame.
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Topics: Motion estimation (67%), Motion field (65%), Match moving (64%) ...read more

3,063 Citations


Open accessBook
19 Dec 2003-
Abstract: About the Author.Foreword.Preface.Glossary.1. Introduction.2. Video Formats and Quality.3. Video Coding Concepts.4. The MPEG-4 and H.264 Standards.5. MPEG-4 Visual.6. H.264/MPEG-4 Part 10.7. Design and Performance.8. Applications and Directions.Bibliography.Index.

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Topics: Multiview Video Coding (81%), Video processing (78%), Video compression picture types (76%) ...read more

2,490 Citations


Open accessJournal ArticleDOI: 10.1109/TCOM.1981.1094950
Abstract: A new technique for estimating interframe displacement of small blocks with minimum mean square error is presented. An efficient algorithm for searching the direction of displacement has been described. The results of applying the technique to two sets of images are presented which show 8-10 dB improvement in interframe variance reduction due to motion compensation. The motion compensation is applied for analysis and design of a hybrid coding scheme and the results show a factor of two gain at low bit rates.

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Topics: Motion compensation (57%), Quarter-pixel motion (57%), Motion estimation (56%) ...read more

1,867 Citations


Journal ArticleDOI: 10.1109/79.733497
Gary J. Sullivan1, Thomas WiegandInstitutions (1)
Abstract: The rate-distortion efficiency of video compression schemes is based on a sophisticated interaction between various motion representation possibilities, waveform coding of differences, and waveform coding of various refreshed regions. Hence, a key problem in high-compression video coding is the operational control of the encoder. This problem is compounded by the widely varying content and motion found in typical video sequences, necessitating the selection between different representation possibilities with varying rate-distortion efficiency. This article addresses the problem of video encoder optimization and discusses its consequences on the compression architecture of the overall coding system. Based on the well-known hybrid video coding structure, Lagrangian optimization techniques are presented that try to answer the question: what part of the video signal should be coded using what method and parameter settings?.

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Topics: Multiview Video Coding (71%), Video compression picture types (69%), Motion compensation (68%) ...read more

1,829 Citations


Open access
01 Jan 1981-
Topics: Inter frame (70%), Motion compensation (67%)

1,665 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
2021168
2020317
2019356
2018349
2017475

Top Attributes

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Topic's top 5 most impactful authors

Marta Karczewicz

121 papers, 4.3K citations

Wen Gao

86 papers, 1.2K citations

Feng Wu

62 papers, 1.5K citations

Thomas Wiegand

61 papers, 6.8K citations

Bernd Girod

57 papers, 3.6K citations

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