# Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds

## TL;DR

This paper presents a new computational model inspired by insect vision that improves small target motion detection in cluttered backgrounds by integrating small-field and wide-field visual cues to reduce false positives.

## Contribution

The paper introduces a novel visual system model that separately extracts and integrates small-field and wide-field motion features for enhanced detection accuracy.

## Key findings

- Outperforms existing models in detection rates
- Effectively reduces false positives
- Utilizes separate motion-sensitive neurons for feature extraction

## Abstract

The robust detection of small targets against cluttered background is important for future artificial visual systems in searching and tracking applications. The insects' visual systems have demonstrated excellent ability to avoid predators, find prey or identify conspecifics - which always appear as small dim speckles in the visual field. Build a computational model of the insects' visual pathways could provide effective solutions to detect small moving targets. Although a few visual system models have been proposed, they only make use of small-field visual features for motion detection and their detection results often contain a number of false positives. To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds. Compared to the existing models, the small-field and wide-field visual features are separately extracted by two motion-sensitive neurons to detect small target motion and background motion. These two types of motion information are further integrated to filter out false positives. Extensive experiments showed that the proposed model can outperform the existing models in terms of detection rates.