Projects

Enhanced pedestrian and vehicle detection using surround-view camera systems in Rearview Camera Images


Source Organization:
University:Carnegie Mellon University
Principal Investigator:Vijayakumar Bhagavatula
PI Contact Information:kumar@ece.cmu.edu
Project Manager:Courtney Ehrlichman
Funding Source(s) and Amounts Provided (by each agency or organization):$55,219
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Start and End Dates:January 2016 - January 2017
Project Status:Active
Subject Categories:In-Vehicle Technologies
Abstract:Being informed of the objects in the vicinity of a vehicle is critical to maintaining safety while driving. Vision is one of the major sources of sensing when humans are driving. However, many traffic accidents (e.g., backup crashes in parking lots and driveways, lane merging accidents on urban roads and highways, etc.) are caused by inadequate visibility. Vehicle camera systems can assist in improving the situational awareness for the drivers. Reduced driver attention is another major cause of accidents. Even with adequate visualization of the vehicle surroundings, drivers may still ignore such information and not take the necessary safety actions. Such attention lapses can be due to many factors, such as fatigue, alcohol, texting and other distractions. In such situations, computer vision techniques that analyze the images for potential safety problems are likely to significantly reduce the chances and impacts of accidents. Many current automotive vision approaches employ cameras that look in front of the cars or behind the cars whereas some others employ vision systems looking at the blind zones. While such vision systems can improve safety by detecting objects in their separate fields of view, these vision systems work independently. By using an integrated surround view camera system that employs four synchronized cameras (covering 360 degrees), we can obtain a bird’s eye view of the vehicle surroundings. Resulting integrated surround-view images allow us to achieve improved object (e.g., vehicle, pedestrian) detection performance in challenging scenarios (e.g., when a pedestrian may be too close to the vehicle, presence of other vehicles in adjacent narrow lanes, etc.). The goal of this project is to develop algorithms that detect and track objects (in particular, pedestrians and vehicles) in these integrated surround-view images and videos and demonstrate superior object detection performance that such systems offer.
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