Are All the Frames Equally Important?

Abstract

In this work, we address the problem of measuring and predicting temporal video saliency -a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.

Glimpse: A Gaze-Based Measure of Temporal Salience

“…The qualitative results reported in [ 8 ] (Figure 3) include the salience scores for 250 frames of videos and ( Figure 5 ). Like our approach, their salience scores in video are higher in accordance to relevant video events.…”

“…Finally, researchers have sought different annotation approaches for understanding and predicting visual attention, mostly focused on static images [37]. Crowdsourcing techniques such as the Restricted Focus Viewer [38] or BubbleView [39] have emerged as a poor man's eye tracker [40] to collect data at a large scale, where the computer display is blurred and the user has to move or click their mouse in order to see a small region in focus [8,41]. User-unknown limited mouse clicks [8] require the same participant to watch the same contents several times.…”

“…Crowdsourcing techniques such as the Restricted Focus Viewer [38] or BubbleView [39] have emerged as a poor man's eye tracker [40] to collect data at a large scale, where the computer display is blurred and the user has to move or click their mouse in order to see a small region in focus [8,41]. User-unknown limited mouse clicks [8] require the same participant to watch the same contents several times. This brings more reliable annotation, but challenges its scalability in terms of the length or number of videos.…”

“…The importance of temporal salience to gain valuable insights about a video structure has been recently noted, and a mouse click-based interaction model was proposed to annotate datasets in the absence of an eye tracker [ 8 ]. However, the approach requires significant manual work and recommends several passes over the same video to ensure a low intra-observer variability and obtain more reliable estimates.…”

Predicting Video Saliency Using Crowdsourced Mouse-Tracking Data

“…Sidorov et al [13] addressed the problem of temporal saliency of video, i.e. how a whole frame is important for viewers.…”