A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

Wang, Hongxin and Peng, Jigen and Zheng, Xuqiang and Yue, Shigang (2019) A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds. IEEE Transactions on Neural Networks and Learning Systems . ISSN 2162-237X

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A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds
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Abstract

Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey, which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems--ommatidia, motion pathway, contrast pathway, and mushroom body. Compared with the existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated into the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over the existing STMD-based models against fake features.

Keywords:Background motion, cluttered natural environment, neural modeling, small target motion detector (STMD), visual system model
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:36114
Deposited On:17 Jun 2019 15:37

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