A feature-based recognition scheme for traffic scenes

@inproceedings{citeulike:889108, abstract = {This paper describes a method for the interpretation of traffic scenes based on the detection and recognition of those objects, or classes of objects which are typically found in an urban scene. Since generic model-based recognition schemes are unsuitable for the analysis of traffic scenes and result in very poor performances, each of the different classes of objects which we expect to find in a typical scene is identified according to some selected features. After identifying the object, its main parameters are computed and, when needed, the object is further classified. The classes of objects we have considered included the roadbed, vehicles, buildings, trees, crosswalks and road signs. The method described here has been successfully tested on a wide set of images of traffic scenes and provided a general-purpose reconstruction of the whole traffic scene as viewed by the driver}, author = {Parodi, P. and Piccioli, G. }, citeulike-article-id = {889108}, journal = {Intelligent Vehicles '95 Symposium., Proceedings of the}, keywords = {computervision, roadsign, traffic}, pages = {229--234}, posted-at = {2006-10-08 02:47:45}, priority = {4}, title = {A feature-based recognition scheme for traffic scenes}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=528285}, year = {1995} }

TY - CONF ID - citeulike:889108 L3 - citeulike-article-id:889108 TI - A feature-based recognition scheme for traffic scenes JF - Intelligent Vehicles '95 Symposium., Proceedings of the SP - 229 EP - 234 N2 - This paper describes a method for the interpretation of traffic scenes based on the detection and recognition of those objects, or classes of objects which are typically found in an urban scene. Since generic model-based recognition schemes are unsuitable for the analysis of traffic scenes and result in very poor performances, each of the different classes of objects which we expect to find in a typical scene is identified according to some selected features. After identifying the object, its main parameters are computed and, when needed, the object is further classified. The classes of objects we have considered included the roadbed, vehicles, buildings, trees, crosswalks and road signs. The method described here has been successfully tested on a wide set of images of traffic scenes and provided a general-purpose reconstruction of the whole traffic scene as viewed by the driver KW - computervision KW - roadsign KW - traffic AU - Parodi, P AU - Piccioli, G PY - 1995/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=528285 ER -