Wang, Hongxin, Peng, Jigen, Fu, Qinbing , Wang, Huatian and Yue, Shigang (2019) Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds. In: The 2019 International Joint Conference on Neural Networks (IJCNN), 14-19 July 2019, Budapest, Hungary.
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Hx.Wang_IJCNN_2019.pdf - Whole Document 3MB |
Item Type: | Conference or Workshop contribution (Presentation) |
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Item Status: | Live Archive |
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.
Keywords: | Small target motion detection, neural modelling, visual cue integration, cluttered background |
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Subjects: | G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G730 Neural Computing G Mathematical and Computer Sciences > G700 Artificial Intelligence |
Divisions: | College of Science > School of Computer Science |
ID Code: | 35684 |
Deposited On: | 16 Apr 2019 15:27 |
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