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Advanced Driver-Assistance Systems (ADAS) have been attracting attention frommany researchers. Vision-based sensors are the closest way to emulate humandriver visual behavior while driving. In this paper, we explore possible waysto use visual attention (saliency) for object detection and tracking. Weinvestigate: 1) How a visual attention map such as a \emph{subjectness}attention or saliency map and an \emph{objectness} attention map can facilitateregion proposal generation in a 2-stage object detector; 2) How a visualattention map can be used for tracking multiple objects. We propose a neuralnetwork that can simultaneously detect objects as and generate objectness andsubjectness maps to save computational power. We further exploit the visualattention map during tracking using a sequential Monte Carlo probabilityhypothesis density (PHD) filter. The experiments are conducted on KITTI andDETRAC datasets. The use of visual attention and hierarchical features hasshown a considerable improvement of $\approx$8\% in object detection whicheffectively increased tracking performance by $\approx$4\% on KITTI dataset.

Utilising Visual Attention Cues for Vehicle Detection and Tracking