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Emovideo : automatic prediction of emotional responses to videos using its cinematic features
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Orientador(es)
Resumo(s)
Movies can evoke intense emotions in their audiences and are often used in the field
of psychology to study emotion. Predicting how video content affects viewer’s emotional
response has become a popular area of research over the past years due to its extensive
applications. In order to provoke an emotional response, film makers use a variety of
techniques while of filming and editing a movie. However, there are not many studies
on how these techniques affect viewer’s emotional responses and if they can be used
solely to predict these responses. With this work we developed a solution that predicts
emotional responses to videos using cinematic features. To accomplish this goal, we
started by studying cinematic techniques and their effects on viewer’s emotions. With
this information, we decided to focus on shot length, key lighting, shot type and camera
movement in this work. Then, we experimented with different methods to extract these
features from videos. First, we used a trained model to segment videos into shots, and
then segmented these shots into frames, key frames, dense flow frames and frames with
the subject and background isolated. The key lighting was calculated from the contrast of
each key frame. We trained and tested models to classify shot type and camera movement,
with different Convolutional Neural Networks, parameters, types of frames and labels.
The best results achieved were 81% for shot type and 89% for camera movement. Lastly,
we created models to predict valence and arousal values with classification and regression
algorithms using all the extracted cinematic features. Overall, our method had results
close to previous works, especially with error metrics, with only cinematic features. This
shows they affect viewers’ valence and arousal and can be a tool in predicting exact values,
while providing interpretability.
Descrição
Tese de mestrado, Engenharia Informática (Engenharia de Software), Universidade de Lisboa, Faculdade de Ciências, 2022
Palavras-chave
features cinemáticas classificação de emoções estimação de emoções rede neural convolucional análise afetiva de conteúdo de vídeo Teses de mestrado - 2022