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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyLeónHira:2012:StDyCa
TitleVehicle Detection using Mixture of Deformable Parts Models: Static and Dynamic Camera
FormatDVD, On-line.
Access Date2021, Jan. 24
Number of Files1
Size10755 KiB
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Author1 León, Leissi Margarita Castañeda
2 Hirata Junior, Roberto
Affiliation1 Institute of Mathematics and Statistics
2 Institute of Mathematics and Statistics
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:29 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsMixture of deformable part models, vehicle detection.
AbstractVehicle detection in video is an important problem in Computer Vision because of the potential applications in security, vehicle traffic, driving assistance and so on. In this work, we used Mixture of Deformable Part Models (MDPM) for vehicle detection in video sequences obtained from static and dynamic cameras. The MDPM method was originally proposed by Felzenszwalb et al in the realm of object detection in images. We tested this method in the realm of video sequences for vehicle detection. We designed a set of experiments that explore the number of components of the mixture and the number of parts model. We performed a comparison study of symmetric and asymmetric MDPMs for vehicle detection. Our findings show that not only the MDPM performed well in vehicle detection in video, but also the best number of components and parts model confirmed the number suggested in Felzenzwalb et al's paper. Finally, the results show some differences between the symmetric and asymmetric MDPMs in vehicle video detection considering different scenarios.
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