A Robust Visual System for Looming Cue Detection Against Translation Motion

Lei, Fang, Peng, Zhiping, Liu, Mei , Peng, Jigen, Cutsuridis, Vassilis and Yue, Shigang (2022) A Robust Visual System for Looming Cue Detection Against Translation Motion. IEEE Transactions on Neural Networks and Learning Systems . ISSN 2162-2388

Full content URL: https://doi.org/10.1109/TNNLS.2022.3149832

A Robust Visual System for Looming Cue Detection Against Translation Motion

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Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to
monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON–OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events—the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.

Keywords:Lobula giant movement detector (LGMD1), looming detection, ON/OFF neural competition, translating motion, Fly visual neural systems
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:49162
Deposited On:26 Jul 2022 14:37

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