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GTC ON-DEMAND

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Abstract:

In this study, we investigate the use of a programmable graphics processing unit (GPU) as an embedded processor for real-time recognition of speed limit signs on the road. The input to our system is a video sequence of the road taken from a moving vehicle. We process this video in real-time and determine if there are any speed limit signs present in the scene and, if so, we recognize and output the number indicated by the sign. The main goal of the recognition system is to operate in real time on a resource-constrained embedded system. Therefore, we first examine the merits and demerits of mapping algorithms often used for speed-limit recognition on to the GPU. Through this process, we find techniques that benefit significantly from the GPU architecture and eliminate algorithms that do not map efficiently on it. We then implement and analyze two sign detection schemes: one feature-based, one template-based. From the results of our experiments, we make several important conclusions about the trade-off between recognition rates and performance. We also make an estimate for the amount of hardware resources needed to perform the recognition in real-time."

In this study, we investigate the use of a programmable graphics processing unit (GPU) as an embedded processor for real-time recognition of speed limit signs on the road. The input to our system is a video sequence of the road taken from a moving vehicle. We process this video in real-time and determine if there are any speed limit signs present in the scene and, if so, we recognize and output the number indicated by the sign. The main goal of the recognition system is to operate in real time on a resource-constrained embedded system. Therefore, we first examine the merits and demerits of mapping algorithms often used for speed-limit recognition on to the GPU. Through this process, we find techniques that benefit significantly from the GPU architecture and eliminate algorithms that do not map efficiently on it. We then implement and analyze two sign detection schemes: one feature-based, one template-based. From the results of our experiments, we make several important conclusions about the trade-off between recognition rates and performance. We also make an estimate for the amount of hardware resources needed to perform the recognition in real-time."

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Topics:
Intelligent Machines, IoT & Robotics, General Interest
Type:
Talk
Event:
GTC Silicon Valley
Year:
2009
Session ID:
S09100
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