Humans receive large quantity of information from the environment with sight and hearing. To help us to react rapidly and properly, there exist mechanisms in the brain to bias attention towards particular regions, namely the salient regions. This attentional bias is not only influenced by vision, but also influenced by audio-visual interaction. According to existing literature, the visual attention can be studied towards eye movements, however the sound effect on eye movement in videos is little known. The aim of this thesis is to investigate the influence of sound in videos on eye movement and to propose an audio-visual saliency model to predict salient regions in videos more accurately. For this purpose, we designed a first audio-visual experiment of eye tracking. We created a database of short video excerpts selected from various films. These excerpts were viewed by participants either with their original soundtrack (AV condition), or without soundtrack (V condition). We analyzed the difference of eye positions between participants with AV and V conditions. The results show that there does exist an effect of sound on eye movement and the effect is greater for the on-screen speech class. Then, we designed a second audio-visual experiment with thirteen classes of sound. Through comparing the difference of eye positions between participants with AV and V conditions, we conclude that the effect of sound is different depending on the type of sound, and the classes with human voice (i.e. speech, singer, human noise and singers classes) have the greatest effect. More precisely, sound source significantly attracted eye position only when the sound was human voice. Moreover, participants with AV condition had a shorter average duration of fixation than with V condition. Finally, we proposed a preliminary audio-visual saliency model based on the findings of the above experiments. In this model, two fusion strategies of audio and visual information were described: one for speech sound class, and one for musical instrument sound class. The audio-visual fusion strategies defined in the model improves its predictability with AV condition.
