# Attention and Prediction Guided Motion Detection for Low-Contrast Small Moving Targets

arXiv: https://arxiv.org/abs/2104.13018 · HTML: https://mlsanity.com/paper/2104.13018/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors](https://mlsanity.com/paper/1910.07960/) (2019)
- [A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds](https://mlsanity.com/paper/1904.04363/) (2019)
- [A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds](https://mlsanity.com/paper/1801.06687/) (2018)
- [Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review](https://mlsanity.com/paper/1904.02048/) (2019)
- [Force-based Algorithm for Motion Planning of Large Agent Teams](https://mlsanity.com/paper/1909.05415/) (2019)
- [A Bio-inspired Collision Detecotr for Small Quadcopter](https://mlsanity.com/paper/1801.04530/) (2018)

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

- [A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case](https://mlsanity.com/paper/2501.02982/) (2025)
- [vSTMD: Visual Motion Detection for Extremely Tiny Target at Various Velocities](https://mlsanity.com/paper/2501.13054/) (2025)
- [Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion](https://mlsanity.com/paper/2605.05804/) (2026)
- [TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit](https://mlsanity.com/paper/2404.00855/) (2024)
- [CA-YOLO: Cross Attention Empowered YOLO for Biomimetic Localization](https://mlsanity.com/paper/2602.07523/) (2026)
- [MITE-Net: SWaP-Optimized 4K Video Tiny Target Perception for Embodied Edge SAR](https://mlsanity.com/paper/2608.15830/) (2026)

## Abstract (arXiv)

Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments where small targets generally exhibit extremely low contrast against neighbouring backgrounds. In this paper, we develop an attention and prediction guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely, an attention module, an STMD-based neural network, and a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.