  Advanced Driver-Assistance Systems (ADAS) have been attracting attention from
many researchers. Vision-based sensors are the closest way to emulate human
driver visual behavior while driving. In this paper, we explore possible ways
to use visual attention (saliency) for object detection and tracking. We
investigate: 1) How a visual attention map such as a \emph{subjectness}
attention or saliency map and an \emph{objectness} attention map can facilitate
region proposal generation in a 2-stage object detector; 2) How a visual
attention map can be used for tracking multiple objects. We propose a neural
network that can simultaneously detect objects as and generate objectness and
subjectness maps to save computational power. We further exploit the visual
attention map during tracking using a sequential Monte Carlo probability
hypothesis density (PHD) filter. The experiments are conducted on KITTI and
DETRAC datasets. The use of visual attention and hierarchical features has
shown a considerable improvement of $\approx$8\% in object detection which
effectively increased tracking performance by $\approx$4\% on KITTI dataset.
