Optimized hierarchical block matching for fast and accurate image registration

Je, Changsoo; Park, Hyung‐Min

doi:10.1016/j.image.2013.04.002

Cited by 46 publications

(17 citation statements)

References 37 publications

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“…The supported prediction block sizes range from 4 × 4 to 16 × 16 samples. Video encoder adopts a block matching algorithm [50,31,49,20] to find a block similar to the one it is encoding on a previously encoded frame. The absolute source position and destination position of a macroblock are stored in the motion vectors.…”

Section: Motion Vectors and Residual Errorsmentioning

confidence: 99%

Fast Object Detection in Compressed Video

Wang

Deng

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Object detection in videos has drawn increasing attention since it is more practical in real scenarios. Most of the deep learning methods use CNNs to process each decoded frame in a video stream individually. However, the free of charge yet valuable motion information already embedded in the video compression format is usually overlooked. In this paper, we propose a fast object detection method by taking advantage of this with a novel Motion aided Memory Network (MMNet). The MMNet has two major advantages: 1) It significantly accelerates the procedure of feature extraction for compressed videos. It only need to run a complete recognition network for I-frames, i.e. a few reference frames in a video, and it produces the features for the following P frames (predictive frames) with a light weight memory network, which runs fast; 2) Unlike existing methods that establish an additional network to model motion of frames, we take full advantage of both motion vectors and residual errors that are freely available in video streams. To our best knowledge, the MMNet is the first work that investigates a deep convolutional detector on compressed videos. Our method is evaluated on the large-scale Ima-geNet VID dataset, and the results show that it is 3× times faster than single image detector R-FCN and 10× times faster than high-performance detector MANet at a minor accuracy loss.

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Section: Motion Vectors and Residual Errorsmentioning

confidence: 99%

Fast Object Detection in Compressed Video

Wang

Deng

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…A large number of block matching algorithms have been proposed over the last decades, such as the traditional methods found in [2]- [5], [7], [11], [15]- [16] and [24]. Amongst the available block matching algorithms, full search leads to the best possible match of the block in the reference frame with a block in another frame by calculating the cost function at each possible location in the search window.…”

Section: Literature Reviewmentioning

confidence: 99%

High Performance Block Matching Algorithm for High Bit-Rate Real-Time Video Communication

Al-Najdawi¹

2018

JJCIT

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“…Алгоритм сопоставления блоков [8] разделяет предыдущий и текущий кадры на непересе-кающиеся макроблоки и сравнивает блоки с предыдущего кадра с соседними блоками на те-кущем кадре. Наилучшее совпадение определяет вектор перемещения макроблока из одного места в другое.…”

Section: отслеживание объектовunclassified

Algorithms for Detecting Incident for Automated Video Surveillance Systems

Gordin¹,

Ivanov²

2017

VNSU

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Optimized hierarchical block matching for fast and accurate image registration

Cited by 46 publications

References 37 publications

Fast Object Detection in Compressed Video

Fast Object Detection in Compressed Video

High Performance Block Matching Algorithm for High Bit-Rate Real-Time Video Communication

Algorithms for Detecting Incident for Automated Video Surveillance Systems

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