Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5484
Title: Detection and classification of vehicles from omnidirectional videos using temporal average of silhouettes
Authors: Karaimer, Hakkı Can
Baştanlar, Yalın
Karaimer, Hakkı Can
Baştanlar, Yalın
Keywords: Object detection
Omnidirectional cameras
Omnidirectional video
Vehicle classification
Vehicle detection
Issue Date: 2015
Publisher: INSTICC
Source: Karaimer, H. C., and Baştanlar, Y. (2015). Detection and classification of vehicles from omnidirectional videos using temporal average of silhouettes. In J. Braz (Ed.), Paper presented at the 10th International Conference on Computer Vision Theory and Applications, VISAPP 2015, Berlin, Germany; 11-14 March (pp.197-204). Setúbal, Portugal: SciTePress.
Abstract: This paper describes an approach to detect and classify vehicles in omnidirectional videos. The proposed classification method is based on the shape (silhouette) of the detected moving object obtained by background subtraction. Different from other shape based classification techniques, we exploit the information available in multiple frames of the video. The silhouettes extracted from a sequence of frames are combined to create an 'average' silhouette. This approach eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types that we worked on are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. The decision boundaries in the feature space are determined using a training set, whereas the performance of the proposed classification is measured with a test set. To ensure randomization, the procedure is repeated with the whole dataset split differently into training and testing samples. The results indicate that the proposed method of using average silhouettes performs better than using the silhouettes in a single frame.
Description: 10th International Conference on Computer Vision Theory and Applications, VISAPP 2015; Berlin; Germany; 11 March 2015 through 14 March 2015
URI: http://hdl.handle.net/11147/5484
ISBN: 9789897580901
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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