TY - GEN
T1 - Toward the detection of urban infrastructure's edge shadows
AU - Isaza, Cesar
AU - Salas, Joaquin
AU - Raducanu, Bogdan
N1 - Funding Information:
★ This research was partially supported with grants from CONACYT 51005), SIP-IPN (20101526) and the Fulbright Scholarship Board.
PY - 2010
Y1 - 2010
N2 - In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising.
AB - In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising.
KW - background modelling
KW - edge detection
KW - shadow segmentation
UR - http://www.scopus.com/inward/record.url?scp=78650878370&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17688-3_4
DO - 10.1007/978-3-642-17688-3_4
M3 - Contribución a la conferencia
SN - 3642176879
SN - 9783642176876
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 37
BT - Advanced Concepts for Intelligent Vision Systems - 12th International Conference, ACIVS 2010, Proceedings
T2 - 12th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2010
Y2 - 13 December 2010 through 16 December 2010
ER -