A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds

• URL: http://arxiv.org/abs/2103.00848v1
• Date: Mon, 1 Mar 2021 08:44:27 GMT
• Title: A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds
• Authors: Xiao Huang, Hong Qiao, Hui Li and Zhihong Jiang
• Abstract summary: This paper proposes a bioinspired neural network based on a new neuro-based motion filtering and multiform 2-D spatial filtering. It can estimate motion direction accurately via only two signals and respond to small targets of different sizes and velocities. It can also extract the information of motion direction and motion accurately energy and rapidly.
• Score: 19.93930316898735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities by adjusting the dendrite field size of the spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly.

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