Utilising Visual Attention Cues for Vehicle Detection and Tracking

TL;DR

This paper explores the use of visual attention maps for vehicle detection and tracking, proposing a neural network that improves detection accuracy and tracking performance by leveraging saliency and objectness cues.

Contribution

It introduces a neural network that jointly detects objects and generates attention maps, enhancing detection and tracking efficiency in ADAS applications.

Findings

8% improvement in object detection accuracy

4% increase in tracking performance

Effective use of visual attention maps in real datasets

Abstract

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.