An Approach to Scene Change Detection

Bieda, I.; Kisil, Anton; Panchenko, T. V.

doi:10.1109/idaacs53288.2021.9660887

Cited by 2 publications

(1 citation statement)

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“…To detect shot boundaries in the video corpus, a content‐aware algorithm from the PySceneDetect package (Castellano, 2022), which relies on changes in HSV (hue, saturation, value) color space, was used. The shot detector achieved reasonable accuracy on a noisy video data set (precision = 0.961 and recall = 0.721) (Bieda et al, 2021).…”

Section: The Current Studymentioning

confidence: 99%

What makes video‐based academic lectures difficult for language learners to comprehend? The role of multimodal complexity

Alghamdi

2024

Foreign Language Annals

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By taking a multidisciplinary, evidence‐based approach to modeling video difficulty, this study investigates the impact of multimodal complexity on language learners' self‐ratings of video difficulty, while simultaneously accounting for the effects of learner differences and video production styles. A set of 320 instructional videos from the corpus of second language video complexity were analyzed using sophisticated natural language processing and computer vision algorithms to extract and compute a wide range of multimodal complexity indices. The results of a linear mixed‐effects model demonstrated that pitch variation and academic spoken formulaic sequences helped to facilitate viewing comprehension, whereas infrequent words, image clutter, the number of visual objects, salient objects, visual texts, shots, and moving objects all impeded viewing comprehension. This study concludes by presenting a number of practical implications that can prove useful for English as a Foreign Language teachers and practitioners alongside a comprehensive agenda for future research in this area.

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Section: The Current Studymentioning

confidence: 99%