Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Omnidirectional camera

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  • Conference Object
    Zamanda ortalaması alınmış ikili önplan imgeleri kullanarak taşıt sınıflandırması
    (IEEE, 2015) Karaimer, Hakkı Can; Baştanlar, Yalın
    We describe a shape-based method for classification of vehicles from omnidirectional videos. Different from similar approaches, the binary images of vehicles obtained by background subtraction in a sequence of frames are averaged over time. We show with experiments that using the average shape of the object results in a more accurate classification than using a single frame. The vehicle types we classify are motorcycle, car and van. We created an omnidirectional video dataset and repeated experiments with shuffled train-test sets to ensure randomization.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 13
    Classification and Tracking of Traffic Scene Objects With Hybrid Camera Systems
    (Institute of Electrical and Electronics Engineers Inc., 2018) Barış, İpek; Baştanlar, Yalın
    In a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.