Traffic surveillance using computer vision techniques is an
emerging research area. Many algorithms are being developed to
detect and track moving vehicles in daytime in effective manner.
However, little work is done for nighttime traffic scenes. For
nighttime, vehicles are identified by detecting and locating vehicle
headlights and rear lights. In this paper, an effective method for
detecting and tracking moving vehicles in nighttime is proposed.
The proposed method identifies vehicles by detecting and locating
vehicle lights using automatic thresholding and connected
components extraction. Detected lamps are then paired using rule
based component analysis approach and tracked using Kalman
Filter (KF). The automatic thresholding approach provides a robust
and adaptable detection process that operates well under various
nighttime illumination conditions. Moreover, most nighttime
tracking algorithms detect vehicles by locating either headlights or
rear lights while the proposed method has the ability to track
vehicles through detecting vehicle headlights and/or rear lights.
Experimental results demonstrate that the proposed method is
feasible and effective for vehicle detection and identification in
various nighttime environments. |
