# Utilising Visual Attention Cues for Vehicle Detection and Tracking

arXiv: https://arxiv.org/abs/2008.00106 · HTML: https://mlsanity.com/paper/2008.00106/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [FBLNet: FeedBack Loop Network for Driver Attention Prediction](https://mlsanity.com/paper/2212.02096/) (2022)
- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Vehicle Detection Performance in Nordic Region](https://mlsanity.com/paper/2403.15017/) (2024)
- [DriverGaze360: OmniDirectional Driver Attention with Object-Level Guidance](https://mlsanity.com/paper/2512.14266/) (2025)
- [Robust and Fast Vehicle Detection using Augmented Confidence Map](https://mlsanity.com/paper/2304.14462/) (2023)
- [AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction](https://mlsanity.com/paper/2407.17442/) (2024)

## Abstract (arXiv)

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 subjectness attention or saliency map and an 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 ≈8% in object detection which effectively increased tracking performance by ≈4% on KITTI dataset.