University of Leicester
Browse

File(s) stored somewhere else

Please note: Linked content is NOT stored on University of Leicester and we can't guarantee its availability, quality, security or accept any liability.

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

journal contribution
posted on 2024-01-04, 12:39 authored by H Wang, J Zhao, C Hu, J Peng, S Yue
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 neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a 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.

Funding

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 12031003, 62103112 and 11771347)

European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie (Grant Number: 691154 (STEP2DYNA) and 778062 (ULTRACEPT))

10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021M700921 and 2019M662837)

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Cybernetics

Volume

53

Issue

10

Pagination

6340 - 6352

Publisher

ieee

issn

2168-2267

eissn

2168-2275

Copyright date

2023

Available date

2024-01-04

Spatial coverage

United States

Language

eng

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC