TY - GEN
T1 - A novel module of tracking vehicles with occlusion
AU - Zhu, Lin
AU - Song, Jingyan
AU - Huang, Qiao
AU - Zhang, Ming
AU - Liu, Hongchao
PY - 2005
Y1 - 2005
N2 - Video surveillance technology has been widely used in Intelligent Transportation System (ITS) to measure traffic flow parameters and detect accidents. Occlusion of moving objects by stationary or other moving foreground objects always causes tracking errors. This paper presents a novel module for vehicle tracking under the condition of occlusion, which can be conveniently added to the existing video surveillance systems as a complementary part. The appearance information of the object (shape and texture) is combined with the spatial-temporal Markov Random Field (MRF) based tracking model through a kind of special structuring elements in Mathematical Morphology (MM) theory. As a result, the sites in MRF are reduced, which saves computational cost greatly, and the assumption on the vehicle shape model in prior is not needed. Experiments on various kinds of image sequences show that the proposed module can effectively track vehicles with severe occlusion.
AB - Video surveillance technology has been widely used in Intelligent Transportation System (ITS) to measure traffic flow parameters and detect accidents. Occlusion of moving objects by stationary or other moving foreground objects always causes tracking errors. This paper presents a novel module for vehicle tracking under the condition of occlusion, which can be conveniently added to the existing video surveillance systems as a complementary part. The appearance information of the object (shape and texture) is combined with the spatial-temporal Markov Random Field (MRF) based tracking model through a kind of special structuring elements in Mathematical Morphology (MM) theory. As a result, the sites in MRF are reduced, which saves computational cost greatly, and the assumption on the vehicle shape model in prior is not needed. Experiments on various kinds of image sequences show that the proposed module can effectively track vehicles with severe occlusion.
UR - http://www.scopus.com/inward/record.url?scp=33745934256&partnerID=8YFLogxK
U2 - 10.1109/IVS.2005.1505219
DO - 10.1109/IVS.2005.1505219
M3 - Conference contribution
AN - SCOPUS:33745934256
SN - 0780389611
SN - 9780780389618
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 894
EP - 899
BT - 2005 IEEE Intelligent Vehicles Symposium, Proceedings
Y2 - 6 June 2005 through 8 June 2005
ER -