In this paper, we investigate the influence of music on human walking behaviors in a public setting monitored by surveillance cameras. To this end, we propose a novel algorithm to characterize the frequency and phase of the walk. It relies on a human-by-detection tracking framework, along with a robust fitting of the human head bobbing motion. Preliminary experiments conducted on more than 100 tracks show that an accuracy greater than 85% for foot strike estimation can be achieved, suggesting that large scale analysis is at reach for finer music/walking behavior relationship studies.