Collaborative eye tracking for image analysis

We present a framework for collaborative image analysis where gaze information is shared across all users. A server gathers and broadcasts fixation data from/to all clients and the clients visualize this information. Several visualization options are provided. The system can run in real-time or gaze information can be recorded and shared the next time an image is accessed. Our framework is scalable to large numbers of clients with different eye tracking devices. To evaluate our system we used it within the context of a spot-the-differences game. Subjects were presented with 10 image pairs each containing 5 differences. They were given one minute to detect the differences in each image. Our study was divided into three sessions. In session 1, subjects completed the task individually, in session 2, pairs of subjects completed the task without gaze sharing, and in session 3, pairs of subjects completed the task with gaze sharing. We measured accuracy, time-to-completion and visual coverage over each image to evaluate the performance of subjects in each session. We found that visualizing shared gaze information by graying out previously scrutinized regions of an image significantly increases the dwell time in the areas of the images that are relevant to the task (i.e. the regions where differences actually occurred). Furthermore, accuracy and time-to-completion also improved over collaboration without gaze sharing though the effects were not significant. Our framework is useful for a wide range of image analysis applications which can benefit from a collaborative approach.