Projects

Automated Detection of Objects in Rear Camera Images


Source Organization:
University:Carnegie Mellon University
Principal Investigator:Vijayakumar Bhagavatula
PI Contact Information:kumar@ece.cmu.edu
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Funding Source(s) and Amounts Provided (by each agency or organization): $87,371.00
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Abstract:An alarming number of fatalities occur every year due to backover i.e. drivers not noticing objects behind their vehicles while reversing. Due to the increased size of the vehicles and the associated blind spots, the number of backover fatalities has been on the rise. According to www.kidsandcars.org, at least 50 children are backed over by cars every week in the US. The objective of the proposed research is to develop automated object detections algorithms from videos captured using rearview cameras. In particular, we will focus on detecting objects behind the vehicle when it is being reversed.

We proposed to develop a robust object detection method based on background subtraction. In ideal conditions, background remains the same between the time when a vehicle is parked and when that vehicle is restarted, resulting in the complete removal of the background and detection of the objects in the field of view. However, in real world conditions, the appearance of the background can change significantly between those two time instants. For example, a driver may park the car in his/her driveway in the evening and pull the car out the next morning. To achieve acceptable object detection performance in real-world conditions, we propose to exploit advanced models such as conditional random field models to achieve tolerance to illumination variations. Another research task to be undertaken is predicting the most likely position for a pedestrian several frames into the future so that the vehicle can avoid a potential collision with pedestrians. To develop and evaluate these methods, we also propose to collect a large database that reflects these real-world challenges.

Successful outcomes of this research will include algorithms that can be implemented in vehicles and produce robust, high-accuracy object detections in images and videos acquired from production-quality rear-cameras.
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