  Recently, the Kernelized Correlation Filters tracker (KCF) achieved
competitive performance and robustness in visual object tracking. On the other
hand, visual trackers are not typically used in multiple object tracking. In
this paper, we investigate how a robust visual tracker like KCF can improve
multiple object tracking. Since KCF is a fast tracker, many can be used in
parallel and still result in fast tracking. We build a multiple object tracking
system based on KCF and background subtraction. Background subtraction is
applied to extract moving objects and get their scale and size in combination
with KCF outputs, while KCF is used for data association and to handle
fragmentation and occlusion problems. As a result, KCF and background
subtraction help each other to take tracking decision at every frame. Sometimes
KCF outputs are the most trustworthy (e.g. during occlusion), while in some
other case, it is the background subtraction outputs. To validate the
effectiveness of our system, the algorithm is demonstrated on four urban video
recordings from a standard dataset. Results show that our method is competitive
with state-of-the-art trackers even if we use a much simpler data association
step.
