A Novel Driving Pattern Recognition and Status Monitoring System.
ABSTRACT This paper describes a novel driving pattern recognition and status monitoring system based on the orientation information.
Two fixed cameras are used to capture the driver’s image and the front-road image. The driver’s sight line and the driving
lane path are found from these 2 captured images and are mapped into a global coordinate. Two correlation coefficients among
the driver’s sight line, the driving lane path and the car heading direction are calculated in the global coordinate to monitor
the driving status such as a safe driving status, a risky driving status and a dangerous driving status. The correlation coefficients
between the lane path and car heading direction in a fixed period are analyzed and recognized as one of 4 driving patterns
by HMM. Four driving patterns including the driving in a straight lane, the driving in a curve lane, the driving of changing
lanes, and the driving of making a turn are able to be recognized so far.
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ABSTRACT: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.02/2004;
Conference Proceeding: Real-time lane detection for autonomous vehicle[show abstract] [hide abstract]
ABSTRACT: A lane detection based on a road model or feature needs correct acquisition of information on the lane in an image. It is inefficient to implement a lane detection algorithm through the full range of an image when it is applied to a real road in real time because of the calculation time. This paper defines two search ranges of detecting a lane in a road. First is the searching mode that searches the lane without any prior information of a road. Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It is allowed to extract accurately and efficiently the edge candidate points of a lane without any unnecessary searching. By means of inverse perspective transform that removes the perspective effect on the edge candidate points, we transform the edge candidate information in the image coordinate system (ICS) into the plane-view image in the world coordinate system (WCS). We define a linear approximation filter and remove faulty edge candidate points by using it. This paper aims to approximate more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fittingIndustrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on; 02/2001
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ABSTRACT: This paper discusses an extension to the inverse perspective mapping geometrical transform to the processing of stereo images and presents the calibration method used on the ARGO autonomous vehicle. The article features also an example of application in the automotive field in which the stereo inverse perspective mapping helps to speed up the process.Image and Vision Computing. 01/1998;