Detection and Classification of Vehicles from Omnidirectional Videos using Temporal Average of Silhouettes

Hakki Can Karaimer, Yalin Bastanlar

2015

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.

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Paper Citation


in Harvard Style

Karaimer H. and Bastanlar Y. (2015). Detection and Classification of Vehicles from Omnidirectional Videos using Temporal Average of Silhouettes . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 197-204. DOI: 10.5220/0005259101970204


in Bibtex Style

@conference{visapp15,
author={Hakki Can Karaimer and Yalin Bastanlar},
title={Detection and Classification of Vehicles from Omnidirectional Videos using Temporal Average of Silhouettes},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={197-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005259101970204},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Detection and Classification of Vehicles from Omnidirectional Videos using Temporal Average of Silhouettes
SN - 978-989-758-090-1
AU - Karaimer H.
AU - Bastanlar Y.
PY - 2015
SP - 197
EP - 204
DO - 10.5220/0005259101970204