A Novel Approach for Video Frame Interpolation using Cubic Motion Compensation Technique

Ghutke, Ragina; Naveen, Ch.; Satpute, V. R.

doi:10.37622/ijaer/11.10.2016.7139-7146

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Motion-based crowd behavior analysis [24] can be done more accurately by optical flow analysis. Optical flow can give a motion present inside a frame, Horn and Schunck [25], Lucas kandes [26] optical flow methods are well known.…”

Section: Related Workmentioning

confidence: 99%

Crowd behavior anomaly detection using correlation of optical flow magnitude

Chakole

Satpute

Cheggoju

2022

J. Phys.: Conf. Ser.

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Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.

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Section: Related Workmentioning

confidence: 99%

Crowd behavior anomaly detection using correlation of optical flow magnitude

Chakole

Satpute

Cheggoju

2022

J. Phys.: Conf. Ser.

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“…Apart from the Middlebury benchmark, there are also two other datasets that are, however, less frequently considered for evaluation. While some interpolation algorithms like [9,17] use the UCF 101 dataset [30] for training and testing, others like [8,40] considered the videos from [33,34].…”

Section: Related Workmentioning

confidence: 99%

Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification

Men,

Hosu,

Lin

et al. 2020

Preprint

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Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, we collected forced choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artifact amplification. From the crowdsourcing data (5887 participants, 3720 comparisons of 50 votes each) we reconstructed absolute quality scale values according to Thurstone's model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 251654672 TRR 161 (Project A05 and B04).

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“…Some interpolation algorithms like [12], [13] used the UCF 101 dataset [14] for training and testing. Others like [11], [15], [16] used the videos from [17], [18]. For evaluation, generally they chose to compute one of MSE, PSNR, and SSIM between their interpolated images and the ground-truth in-between images.…”

Section: Related Workmentioning

confidence: 99%

Visual Quality Assessment for Motion Compensated Frame Interpolation

Men

Hosu

Maurer

et al. 2019

2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)

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Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r.t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.Index Terms-visual quality assessment, optical flow, frame interpolation

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A Novel Approach for Video Frame Interpolation using Cubic Motion Compensation Technique

Cited by 6 publications

References 14 publications

Crowd behavior anomaly detection using correlation of optical flow magnitude

Crowd behavior anomaly detection using correlation of optical flow magnitude

Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification

Visual Quality Assessment for Motion Compensated Frame Interpolation

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