Near range pedestrian collision detection using bio-inspired visual neural networks

Belevskiy, Vladimir and Yue, Shigang (2011) Near range pedestrian collision detection using bio-inspired visual neural networks. In: Natural Computation (ICNC), 2011 Seventh International Conference on, 26-28 July 2011, Shanghai.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


New vehicular safety standards require the development of pedestrian collision detection systems that can trigger the deployment of active impact alleviation measures from the vehicle prior to a collision. In this paper, we propose a new vision-based system for near-range pedestrian collision detection. The low-level system uses a bio-inspired visual neural network, which emulates the visual system of the locust, to detect visual cues relevant to objects in front of a moving car. At a higher level, the system employs a neural-network classifier to identify dangerous pedestrian positions, triggering an alarm signal. The system was tuned via simulation and tested using recorded video sequences of real vehicle impacts. The experiment results demonstrate that the system is able to discriminate between pedestrians in dangerous and safe positions, triggering alarms accordingly.

Keywords:Neural networks
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:12818
Deposited On:06 Jan 2014 11:22

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