This approach to motivation is more subtle than the first approach. Rather than fixing camera positions in a grid, and using the histograms to choose which grid position to move to next, this method uses the difference between the middle histogram and the LRTB (left, right, top, bottom) histograms to create a vector for the next move. The more different the edges are, the larger the movement. The result has a rather obsessive quality. The camera’s gaze tends to obsess about the details of a small area, and eventually (after an indeterminate time) move onto another region to obsess about. Here is a plot of the camera’s movement. It starts in red, and ends up in green:
Notice the clusters where the camera explores the small details of one region. The obvious colour shift in the second from the right cluster indicates the camera spent much more time in that area than in the other clusters. Here is a detail of that area:
Upon closer inspection it seems clear that this cluster is actually two clusters, the second of which (in green) is much more dense. The camera spent 2571 iterations in this cluster alone, where the total iterations was only 4274, representing approximately 60%. In the next run I’ll attempt to increase the likelihood of the gaze to escape these clusters by increasing the length of the vector.
This mock-up shows the path of the camera overlayed on the visual field. The gaze is clearly attracted to areas including many edges, and tend to escape when the vector is aligned with edges in the frame: