Resource type
Date created
2019-04-30
Authors/Contributors
Abstract
In a society of rapidly advancing vehicle safety for passengers, manufacturers con nue to consider the safety of pedestrians as an aerthought. In fact, nearly 1 in 5 people killed in car crashes every year in BC are pedestrians, and most of these deaths are preventable, according to the Insurance Corpora on of Bri sh Columbia. EagleVision's mission is to minimize or greatly reduce night- me vehicular collisions with pedestrians by implemen ng a high-effec ve yet affordable device, which will recognize pedestrians even in the darkest of circumstances and alert the driver. EagleVision will construct this compact and fully embedded device by combining a high quality night-vision sensors, a micro-PC board, and cung-edge self-developed machine learning soware which reacts in real me. Looking from a top-perspec ve, the system will pass in the input data through the camera sensor and be decoded by the on-board CPU of our motherboard. The decoded string will then be sent to the concurrently running AI, which will detect and iden fy pedestrians based on their facial features and send an external audio/visual alert to the user.
Document
Description
Undergraduate Engineering Science students are required to complete a group-based, two-course capstone sequence: ENSC 405W and ENSC 440. Groups form company structures and create an innovative product that potentially acts as a solution to a real-life problem. This collection archives the following assignments: proposal, design specifications, requirements specifications, and proof of concept.
Copyright statement
Copyright is held by the author(s).
Scholarly level
Language
English
Member of collection
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team16_809_9849059_16prop.pdf | 905.24 KB |