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This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one(More)
This paper presents Adaboost learning-based method for license plate detection in unconstrained environment (cluttered scenes, changing illumination, in-plane and out-plane rotation of license plates). Our approach is motivated by the idea that learning-based method can implicitly derive a robust object model through training using large set of positive and(More)
Accurate and fast information acquisition on traffic condition is vital to the urban drivers and city management. Today, most of the computer vision-based techniques in traffic condition monitoring perform on video stream, in which requires high networking bandwidth to transfer the video stream to the processing unit. In this paper, we present a simple yet(More)
This paper presents a human detection system using motion feature and adaptive boosting machine learning algorithm. The system will identify and detect human from the video captured in the outdoor environment. This research consists of three stages. The preliminary stage involves image preprocessing to segment out the motion region by reducing the noise.(More)
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