Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4320
Title: Shape based detection and classification of vehicles using omnidirectional videos
Other Titles: Tümyönlü videolar kullanarak şekil tabanlı araç tespiti ve sınıflandırılması
Authors: Baştanlar, Yalın
Karaimer, Hakkı Can
Karaimer, Hakkı Can
Izmir Institute of Technology. Computer Engineering
Keywords: Flowchart method
K Nearest neighbors
Deep neural networks
Computer vision
Silhouette-based method
Issue Date: Jun-2015
Publisher: Izmir Institute of Technology
Source: Karaimer, Hakkı C. (2015). Shape based detection and classification of vehicles using omnidirectional videos. Unpublished master's thesis, İzmir Institute of Technology, İzmir, Turkey
Abstract: To detect and classify vehicles in omnidirectional videos, an approach based on the shape (silhouette) of the moving object obtained by background subtraction is proposed. Different from other shape based classification techniques, the information available in multiple frames of the video is exploited. Two different approaches were investigated 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 which are classified are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. Three separate methods of classification is applied. The first one is a flowchart based (i.e. rule based) method, the second one is K nearest neighbor classification, and the third one is using a Deep Neural Network. 60% of the samples in the dataset are used for training. To ensure randomization, the procedure is repeated three times with the whole dataset split each time differently into training and testing samples (i.e. three-fold cross validation). The results indicate that using silhouettes in multiple frames performs better than using single frame silhouettes.
Description: Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2015
Text in English; Abstract: Turkish and English
Includes bibliographical references (leaves: 40-44)
xiii, 44 leaves
URI: http://hdl.handle.net/11147/4320
Appears in Collections:Master Degree / Yüksek Lisans Tezleri

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