36,747 research outputs found
Automatically detecting road sign text from natural scene video
Automatic detection of text on road signs can help drivers keep aware of the traffic situation and surrounding environments by reminding them of the signs ahead. Current systems can only detect constrained road signs or produce unsatisfying performance when dealing with complex scenes in practical use. This paper firstly reviews the existing techniques used for text detection from natural scene. A novel system which detects text on road signs from natural scene video is then proposed. Our detailed approaches and methodology give a promising solution to this problem in order to reduce the running time and improve the recognition rate. © 2006 IEEE
Reading Between the Lanes: Text VideoQA on the Road
Text and signs around roads provide crucial information for drivers, vital
for safe navigation and situational awareness. Scene text recognition in motion
is a challenging problem, while textual cues typically appear for a short time
span, and early detection at a distance is necessary. Systems that exploit such
information to assist the driver should not only extract and incorporate visual
and textual cues from the video stream but also reason over time. To address
this issue, we introduce RoadTextVQA, a new dataset for the task of video
question answering (VideoQA) in the context of driver assistance. RoadTextVQA
consists of driving videos collected from multiple countries, annotated
with questions, all based on text or road signs present in the driving
videos. We assess the performance of state-of-the-art video question answering
models on our RoadTextVQA dataset, highlighting the significant potential for
improvement in this domain and the usefulness of the dataset in advancing
research on in-vehicle support systems and text-aware multimodal question
answering. The dataset is available at
http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvq
The Reliability and Effectiveness of a Radar-Based Animal Detection System
This document contains data on the reliability and effectiveness of an animal detection system along U.S. Hwy 95 near Bonners Ferry, Idaho. The system uses a Doppler radar to detect large mammals (e.g., deer and elk) when they approach the highway. The system met most of the suggested minimum norms for reliability. The total time the warning signs were activated was at most 90 seconds per hour, and likely substantially less. Animal detection systems are designed to detect an approaching animal. After an animal has been detected, warning signs are activated which allow drivers to respond. Results showed that 58.1–67.9% of deer were detected sufficiently early for northbound drivers, and 70.4–85% of deer were detected sufficiently early for southbound drivers. The effect of the activated warning signs on vehicle speed was greatest when road conditions were challenging (e.g., freezing temperatures and snow- and ice-covered road surface) and when visibility was low (night). In summer, there was no measurable benefit of activated warning signs, at least not as far as vehicle speed is concerned. Depending on the conditions in autumn and winter, the activated warning signs resulted in a speed reduction of 0.69 to 4.43 miles per hour. The report includes practical recommendations for operation and maintenance of the system and suggestions for potential future research
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