Share Email Print

Proceedings Paper

A robust method for estimating motorbike count based on visual information learning
Author(s): Kien C. Huynh; Dung N. Thai; Sach T. Le; Nam Thoai; Kazuhiko Hamamoto
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Estimating the number of vehicles in traffic videos is an important and challenging task in traffic surveillance, especially with a high level of occlusions between vehicles, e.g.,in crowded urban area with people and/or motorbikes. In such the condition, the problem of separating individual vehicles from foreground silhouettes often requires complicated computation [1][2][3]. Thus, the counting problem is gradually shifted into drawing statistical inferences of target objects density from their shape [4], local features [5], etc. Those researches indicate a correlation between local features and the number of target objects. However, they are inadequate to construct an accurate model for vehicles density estimation. In this paper, we present a reliable method that is robust to illumination changes and partial affine transformations. It can achieve high accuracy in case of occlusions. Firstly, local features are extracted from images of the scene using Speed-Up Robust Features (SURF) method. For each image, a global feature vector is computed using a Bag-of-Words model which is constructed from the local features above. Finally, a mapping between the extracted global feature vectors and their labels (the number of motorbikes) is learned. That mapping provides us a strong prediction model for estimating the number of motorbikes in new images. The experimental results show that our proposed method can achieve a better accuracy in comparison to others.

Paper Details

Date Published: 4 March 2015
PDF: 5 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431T (4 March 2015); doi: 10.1117/12.2180062
Show Author Affiliations
Kien C. Huynh, Ho Chi Minh City Univ. of Technology (Viet Nam)
Dung N. Thai, Ho Chi Minh City Univ. of Technology (Viet Nam)
Sach T. Le, Ho Chi Minh City Univ. of Technology (Viet Nam)
Nam Thoai, Ho Chi Minh City Univ. of Technology (Viet Nam)
Kazuhiko Hamamoto, Tokai Univ. (Japan)

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?