Exemplar-based video inpainting approach using temporal relationship of consecutive frames

Hung, Kuo-Lung; Lai, Shih-Che

doi:10.1109/icawst.2017.8256482

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…In video prediction, the goal is to predict the most probable future frames from a sequence of past observations. There are patch-based approaches [16], probabilistic model based approaches [4] and methods handling background and foreground separately [18,8] for video inpainting. For frame interpolation, there are approaches [2] using dense optical flow field, phase-based method [14], deep learning Fig.…”

Section: Related Workmentioning

confidence: 99%

Multi-View Frame Reconstruction With Conditional Gan

Mahmud

Billah

Roy-Chowdhury

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Multi-view frame reconstruction is an important problem particularly when multiple frames are missing and past and future frames within the camera are far apart from the missing ones. Realistic coherent frames can still be reconstructed using corresponding frames from other overlapping cameras. We propose an adversarial approach to learn the spatio-temporal representation of the missing frame using conditional Generative Adversarial Network (cGAN). The conditional input to each cGAN is the preceding or following frames within the camera or the corresponding frames in other overlapping cameras, all of which are merged together using a weighted average. Representations learned from frames within the camera are given more weight compared to the ones learned from other cameras when they are close to the missing frames and vice versa. Experiments on two challenging datasets demonstrate that our framework produces comparable results with the state-of-the-art reconstruction method in a single camera and achieves promising performance in multi-camera scenario.

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Section: Related Workmentioning

confidence: 99%

Multi-View Frame Reconstruction With Conditional Gan

Mahmud

Billah

Roy-Chowdhury

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…Earlier inpainting was only done for restoring small areas, text removal, filling in holes, removing red eye, etc. The application has been widely spread to modify large areas by retaining the structure and the texture information, video inpainting [1], secret image sharing [2], etc. The aim of image inpainting is to fill the damaged region or unknown region of the image in such a way that inpainted image should seem to be unaltered.…”

Section: Introductionmentioning

confidence: 99%

A qualitative study of Exemplar based Image Inpainting

Shroff

Bombaywala

2019

SN Appl. Sci.

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Image inpainting is exploring the way of filling the missing or left out regions in an image by only having knowledge of surrounding pixel values. One of the block matching algorithms use for image inpainting is exemplar based technique. Exemplar based image inpainting method search the best matching patch for the target region in the image and when it is obtained, the pixel values are copied from the source region and pasted to the target region. The selections of different patch matching criteria, patch size and filling order have a great affect on the results. This paper describes some of the variations in traditional exemplar method which include change in patch selection, priority, patch size and multiresolution inpainting. The advantages and drawbacks for the modified methods are also discussed here.

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A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Exemplar-based video inpainting approach using temporal relationship of consecutive frames

Cited by 4 publications

References 7 publications

Multi-View Frame Reconstruction With Conditional Gan

Multi-View Frame Reconstruction With Conditional Gan

A qualitative study of Exemplar based Image Inpainting

A Comprehensive Survey of Video Datasets for Background Subtraction

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