  Tracking humans in crowded video sequences is an important constituent of
visual scene understanding. Increasing crowd density challenges visibility of
humans, limiting the scalability of existing pedestrian trackers to higher
crowd densities. For that reason, we propose to revitalize head tracking with
Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with
over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For
evaluation, we proposed a new metric, IDEucl, to measure an algorithm's
efficacy in preserving a unique identity for the longest stretch in image
coordinate space, thus building a correspondence between pedestrian crowd
motion and the performance of a tracking algorithm. Moreover, we also propose a
new head detector, HeadHunter, which is designed for small head detection in
crowded scenes. We extend HeadHunter with a Particle Filter and a color
histogram based re-identification module for head tracking. To establish this
as a strong baseline, we compare our tracker with existing state-of-the-art
pedestrian trackers on CroHD and demonstrate superiority, especially in
identity preserving tracking metrics. With a light-weight head detector and a
tracker which is efficient at identity preservation, we believe our
contributions will serve useful in advancement of pedestrian tracking in dense
crowds.
