5 March 2015 Detecting and extracting identifiable information from vehicles in videos
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This paper presents a system to detect and extract identifiable information such as license plates, make, model, color, and bumper stickers present on vehicles. The goal of this work is to develop a system that automatically describes a vehicle just as a person would. This information can be used to improve traffic surveillance systems. The presented solution relies on efficient segmentation and structure of license plates to identify and extract information from vehicles. The system was evaluated on videos captures on Florida highways and is expected to work in other regions with little or no modifications. Results show that license plate was successfully segmented 92% of the cases, the make and the model of the car were segmented out and in 93% of the cases and bumper stickers were segmented in 92.5% of the cases. Over all recognition accuracy was 87%.
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Siddharth Roheda, Siddharth Roheda, Hari Kalva, Hari Kalva, Mehul Naik, Mehul Naik, "Detecting and extracting identifiable information from vehicles in videos", Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 940705 (5 March 2015); doi: 10.1117/12.2083356; https://doi.org/10.1117/12.2083356


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