Guha Balakrishnan

Detecting Pulse from Head Motions in Video

We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions. It then chooses the component that best corresponds to heartbeats based on its temporal frequency spectrum. Finally, we analyze the motion projected to this component and identify peaks of the trajectories, which correspond to heartbeats. When evaluated on 18 subjects, our approach reported heart rates nearly identical to an electrocardiogram device. Additionally we were able to capture clinically relevant information about heart rate variability.

Video diff: highlighting differences between similar actions in videos

When looking at videos of very similar actions with the naked eye, it is often difficult to notice subtle motion differences between them. In this paper we introduce video diffing, an algorithm that highlights the important differences between a pair of video recordings of similar actions. We overlay the edges of one video onto the frames of the second, and color the edges based on a measure of local dissimilarity between the videos. We measure dissimilarity by extracting spatiotemporal gradients from both videos and calculating how dissimilar histograms of these gradients are at varying spatial scales. We performed a user study with 54 people to compare the ease with which users could use our method to find differences. Users gave our method an average grade of 4.04 out of 5 for ease of use, compared to 3.48 and 2.08 for two baseline approaches. Anecdotal results also show that our overlays are useful in the specific use cases of professional golf instruction and analysis of animal locomotion simulations.