Characterization, Detection, and Synchronization of Audio-Video Events in Bharatanatyam Adavus

Mallick, Tanwi; Das, Partha Pratim; Majumdar, A. K.

doi:10.1007/978-981-10-7221-5_12

Cited by 3 publications

(8 citation statements)

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“…The two frame differencing [3]- [7] are the apparent approaches to detect the motion and non-motion frames. However, it requires strict, static, and manually adjustable thresholds, which vary with each video.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…It is done by detecting the onset from the music signal and tracking the beats. Authors in [3], do a similar work, where they can mark the start point of non-motion frames using the onset beat detection method. In this, image differencing and instantaneous acceleration use to segment the KFs.…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…From the literature, we identify some of the contemporary approaches which can be compared with our approach. In [3], the authors tried two image differencing techniques to detect KFs in Bharatanatyam dance which is less effective than our Non-ML approach, where we try with background subtraction. Fig.…”

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

“…7(d) shows comparison. It also shows the number of Adavu variants associated within the braces, that is, a glimpse of the data set used by [3]. In some cases, the current approach tries more variants and more videos (Table -2).…”

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

“…In some cases, the current approach tries more variants and more videos (Table -2). In this comparison, we identify three videos; Sarrika, Tatta & Utsanga which perform better in [3] than the current method.…”

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

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An Automated Method for Identification of Key frames in Bharatanatyam Dance Videos

et al. 2021

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Identifying key frames is the first and necessary step before solving the variety of other Bharatanatyam problems. The paper aims to partition the momentarily stationary frames (key frames) from this dance video's motion frames. The proposed key frames (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance's basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.

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Section: Motivation and Related Workmentioning

confidence: 99%

Section: Motivation and Related Workmentioning

confidence: 99%

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

Section: B a Comparison With Contemporary Approachesmentioning

confidence: 99%

See 3 more Smart Citations