Detection of Text Marks on Moving Vehicles
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Tracking of vehicle trajectory by combining a camera and a laser rangefinder
Machine Vision and Applications
Vs-star: A visual interpretation system for visual surveillance
Pattern Recognition Letters
Journal of Signal Processing Systems
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This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed “on the fly.” The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed