A framework for automatically constructing a dataset for training a vehicle detector
by Changyon Kim; Jeonghwan Gwak; Daeyoung Shim; Moongu Jeon
International Journal of Computational Vision and Robotics (IJCVR), Vol. 9, No. 2, 2019

Abstract: Object detection based on a trained detector has been widely applied to diverse tasks such as pedestrian, face, and vehicle detection. In such approach, detectors are learned offline with an enormous number of training samples. However, the approach has a significant drawback that heavy intervention and effort, as well as domain knowledge, of a human are essentially required to construct a reliable training dataset. To remedy this drawback, we propose a framework to collect and label training samples automatically. By analysing information of foreground blobs obtained from background subtraction results, a training dataset can be constructed without any human's effort. Also, condition investigation of scenes is performed periodically to check the suitability of sample candidates. As a result, it generates an accurate vehicle detector. With the proposed method, training samples can be automatically collected only when vehicle blobs in the given scene provide suitable appearance information. The effectiveness of the proposed framework is demonstrated from vehicle detection tasks under real traffic environments.

Online publication date: Tue, 02-Apr-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com