  Most current multi-object trackers focus on short-term tracking, and are
based on deep and complex systems that often cannot operate in real-time,
making them impractical for video-surveillance. In this paper we present a
long-term, multi-face tracking architecture conceived for working in crowded
contexts where faces are often the only visible part of a person. Our system
benefits from advances in the fields of face detection and face recognition to
achieve long-term tracking, and is particularly unconstrained to the motion and
occlusions of people. It follows a tracking-by-detection approach, combining a
fast short-term visual tracker with a novel online tracklet reconnection
strategy grounded on rank-based face verification. The proposed rank-based
constraint favours higher inter-class distance among tracklets, and reduces the
propagation of errors due to wrong reconnections. Additionally, a correction
module is included to correct past assignments with no extra computational
cost. We present a series of experiments introducing novel specialized metrics
for the evaluation of long-term tracking capabilities, and publicly release a
video dataset with 10 manually annotated videos and a total length of 8' 54".
Our findings validate the robustness of each of the proposed modules, and
demonstrate that, in these challenging contexts, our approach yields up to 50%
longer tracks than state-of-the-art deep learning trackers.
