  Multi-person tracking plays a critical role in the analysis of surveillance
video. However, most existing work focus on shorter-term (e.g. minute-long or
hour-long) video sequences. Therefore, we propose a multi-person tracking
algorithm for very long-term (e.g. month-long) multi-camera surveillance
scenarios. Long-term tracking is challenging because 1) the apparel/appearance
of the same person will vary greatly over multiple days and 2) a person will
leave and re-enter the scene numerous times. To tackle these challenges, we
leverage face recognition information, which is robust to apparel change, to
automatically reinitialize our tracker over multiple days of recordings.
Unfortunately, recognized faces are unavailable oftentimes. Therefore, our
tracker propagates identity information to frames without recognized faces by
uncovering the appearance and spatial manifold formed by person detections. We
tested our algorithm on a 23-day 15-camera data set (4,935 hours total), and we
were able to localize a person 53.2% of the time with 69.8% precision. We
further performed video summarization experiments based on our tracking output.
Results on 116.25 hours of video showed that we were able to generate a
reasonable visual diary (i.e. a summary of what a person did) for different
people, thus potentially opening the door to automatic summarization of the
vast amount of surveillance video generated every day.
