Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in
Crowd Scenes
Abstract
While visual tracking has been greatly improved over the
recent years, crowd scenes remain particularly challenging
for people tracking due to heavy occlusions, high crowd
density, and significant appearance variation. To address
these challenges, we first design a Sparse Kernelized Correlation Filter (S-KCF) to suppress target response variations caused by occlusions and illumination changes, and
spurious responses due to similar distractor objects. We
then propose a people tracking framework that fuses the SKCF response map with an estimated crowd density map
using a convolutional neural network (CNN), yielding a re-
fined response map. To train the fusion CNN, we propose
a two-stage strategy to gradually optimize the parameters. The first stage is to train a preliminary model in batch
mode with image patches selected around the targets, and
the second stage is to fine-tune the preliminary model using the real frame-by-frame tracking process. Our density
fusion framework can significantly improves people tracking in crowd scenes, and can also be combined with other
trackers to improve the tracking performance. We validate
our framework on two crowd video datasets