Automatic Collection of Fuel Prices from a Network of Mobile Cameras
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
A prototype of landmark-based car navigation using a full-windshield head-up display system
AMC '09 Proceedings of the 2009 workshop on Ambient media computing
Dynamic Tracking System through PSO and Parzen Particle Filter
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Road sign detection using eigen color
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A novel approach for captions detection in video sequences
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Distortion invariant road sign detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic detection and recognition of Korean text in outdoor signboard images
Pattern Recognition Letters
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
An approach to the recognition of informational traffic signs based on 2-d homography and SVMs
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Scene text recognition and tracking to identify athletes in sport videos
Multimedia Tools and Applications
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
| Hi-index | 0.00 |
A fast and robust framework for incrementally detecting text on road signs from video is presented in this paper. This new framework makes two main contributions. 1) The framework applies a divide-and-conquer strategy to decompose the original task into two subtasks, that is, the localization of road signs and the detection of text on the signs. The algorithms for the two subtasks are naturally incorporated into a unified framework through a feature-based tracking algorithm. 2) The framework provides a novel way to detect text from video by integrating two-dimensional (2-D) image features in each video frame (e.g., color, edges, texture) with the three-dimensional (3-D) geometric structure information of objects extracted from video sequence (such as the vertical plane property of road signs). The feasibility of the proposed framework has been evaluated using 22 video sequences captured from a moving vehicle. This new framework gives an overall text detection rate of 88.9% and a false hit rate of 9.2%. It can easily be applied to other tasks of text detection from video and potentially be embedded in a driver assistance system.