DocumentCode :
2518436
Title :
Co-training of context models for real-time vehicle detection
Author :
Gepperth, Alexander R T
Author_Institution :
ENSTA ParisTech, Paris, France
fYear :
2012
fDate :
3-7 June 2012
Firstpage :
814
Lastpage :
820
Abstract :
We describe a simple way to reduce the amount of required training data in context-based models of realtime object detection. We demonstrate the feasibility of our approach in a very challenging vehicle detection scenario comprising multiple weather, environment and light conditions such as rain, snow and darkness (night). The investigation is based on a real-time detection system effectively composed of two trainable components: an exhaustive multiscale object detector (”signal-driven detection”), as well as a module for generating object-specific visual attention (”context models”) controlling the signal-driven detection process. Both parts of the system require a significant amount of ground-truth data which need to be generated by human annotation in a time-consuming and costly process. Assuming sufficient training examples for signal-based detection, we demonstrate that a co-training step can eliminate the need for separate ground-truth data to train context models. This is achieved by directly training context models with the results of signal-driven detection. We show that this process is feasible for different qualities of signal-driven detection, and maintains the performance gains from context models. As it is by now widely accepted that signal-driven object detection can be significantly improved by context models, our method allows to train strongly improved detection systems without additional labor, and above all, cost.
Keywords :
lighting; object detection; traffic engineering computing; context models cotraining; context-based models; environment conditions; exhaustive multiscale object detector; ground-truth data; human annotation; light conditions; object-specific visual attention; real-time object detection; real-time vehicle detection; signal-driven detection process; training data; weather conditions; Computational modeling; Context; Context modeling; Data models; Object detection; Training; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2012 IEEE
Conference_Location :
Alcala de Henares
ISSN :
1931-0587
Print_ISBN :
978-1-4673-2119-8
Type :
conf
DOI :
10.1109/IVS.2012.6232306
Filename :
6232306
Link To Document :
بازگشت