Please use this identifier to cite or link to this item:
Title: Vehicle detection under tough conditions using prioritized feature extraction with shadow recognition
Author(s): Siu, Wan Chi 
Author(s): Yang, X.-F.
Issue Date: 2017
Publisher: IEEE
Related Publication(s): 2017 IEEE 22nd International Conference on Digital Signal Processing (DSP) Proceedings
Vehicle detection is the core function in any Driver Assistant System. Besides the challenge in various environmental conditions, the limitation in execution time and computing power is also critical. This paper proposes a shadow detection step that aims at recognizing the shadow part of the train in various environments (including very tough cases) to accelerate the detection process. We propose two shadow recognition approaches for railway trains. In our first approach, we propose a prioritized feature extraction scheme that examines multiple features such as HOG and Color Histogram hierarchically to achieve high robustness as well as preserve the fast detecting speed. Experiments show satisfying results. Subsequently we propose a second approach using machine learning that automatically learns the features and decisions via a modified decision tree classifier with a novel confidence measuring scheme. Experiments show further improvements in both accuracy and execution time.
DOI: 10.1109/ICDSP.2017.8096060
CIHE Affiliated Publication: No
Appears in Collections:CIS Publication

SFX Query Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.