Authors:
Hakki Can Karaimer
and
Yalin Bastanlar
Affiliation:
Izmir Institute of Technology, Turkey
Keyword(s):
Omnidirectional Camera, Omnidirectional Video, Object Detection, Vehicle Detection, Vehicle Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
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 th
e 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.
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