Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6476
Title: Detection and classification of vehicles from omnidirectional videos using multiple silhouettes
Authors: Karaimer, Hakkı Can
Barış, İpek
Baştanlar, Yalın
Karaimer, Hakkı Can
Barış, İpek
Baştanlar, Yalın
Keywords: Object detection
Omnidirectional cameras
Traffic surveillance
Vehicle classification
Vehicle detection
Issue Date: Aug-2017
Publisher: Springer Verlag
Source: Karaimer, H. C., Barış, İ., and Baştanlar, Y. (2017). Detection and classification of vehicles from omnidirectional videos using multiple silhouettes. Pattern Analysis and Applications, 20(3), 893-905. doi:10.1007/s10044-017-0593-z
Abstract: To detect and classify vehicles in omnidirectional videos, we propose an approach based on the shape (silhouette) of the moving object obtained by background subtraction. Different from other shape-based classification techniques, we exploit the information available in multiple frames of the video. We investigated two different approaches for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions for all frames and use consensus of these decisions. Using multiple frames eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types we classify are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity and Hu moments. We applied two separate methods of classification. First one is a flowchart-based method that we developed and the second is K-nearest neighbour classification. 60% of the samples in the dataset are used for training. To ensure randomization in the experiments, threefold cross-validation is applied. The results indicate that using multiple silhouettes increases the classification performance.
URI: http://doi.org/10.1007/s10044-017-0593-z
http://hdl.handle.net/11147/6476
ISSN: 1433-7541
1433-755X
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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