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Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep...
In this work, fast and efficient appearance-based methods for visual loop-closure detection are proposed. The widely used technique based on the Bag-of-Words image representation has shown some limitations especially with aliasing problem. In this work, however, an appearance-based approach for loop closure detection using local invariant and colour features is proposed. The first technique uses Bayes...
Vehicle detection and tracking plays an effective and significant role in the area of traffic surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. Although various researches have been done in this area and many methods have been implemented, still this area has...
Object tracking is an essential task in visual traffic surveillance. Ideally, a tracker should be able to accurately capture an object's natural motion such as translation, rotation, and scaling. However, it is well known that object appearance varies due to changes in viewing angle, scale, and illumination. They introduce ambiguity to the image cue on which a visual tracker usually relies and which...
A new algorithm namely moving cast shadow elimination based on luminance and texture features (MSELT) to detect moving shadows of vehicles is investigated in this paper. Different from traditional methods only performed in color space, we combine the luminance in the CIE Luv color space and texture feature to determine shadows. The proposed algorithm based on Gaussian Mixture Model (GMM) uses the...
Because when moving objects are tracked by mean-shift algorithm, the starting position of target in the current frame starts at the center of the target in the previous frame, so the accuracy of vehicle tracking is not too high. In order to overcome this shortcoming, the mask-based mean-shift algorithm is proposed in the paper. The accuracy of target tracking can be improved by forecasting its starting...
The support vector machine (SVM) provides a robust, accurate and effective technique for pattern recognition and classification. Although the SVM is essentially a binary classifier, it can be adopted to handle multi-class classification tasks. The conventional way to extent the SVM to multi-class scenarios is to decompose an m-class problem into a series of two-class problems, for which either the...
At present, vision-based illegally parked vehicles detection faces a range of issues such as narrowness of detection range, low detection precision and robustness. This paper proposed a technique for illegally parked vehicles detection. Firstly, Omni-Directional Vision Sensors (ODVS) are used to access Omni-directional images of the scene. Secondly, a method based on two backgrounds modeled by Gaussian...
This paper addresses the problem of extracting the road region in different driving environments with dynamic lighting changes, for driver-assistance applications. In this paper, we propose a stereo visual sensor system and a vision-based road extraction method in a new color space. The color space is designed such that it is representative of intrinsic reflectance of the road surface, and independent...
A shadow detection algorithm base on spatial features was proposed, in the case of focusing on traffic vehicle detection system. First of all, multi-foreground rectangles were extracted by using Gaussian mixture model (GMM) and edge detection operator of mathematical morphology. Then, histogram of horizontal location - foreground point number of vertical direction was computed, combined with optimum...
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