Papers by Keyword: Background Difference

Abstract: Vehicle detection based on video plays a more and more important role in the intelligent transportation system (ITSs), since it presents siginificant advantages such as simple structure, less equipment, no radiation and easier to secondary development and so on over other methods. Based on those the paper summarizes the principle of video velocity measurement and object detection, furthermore, focuses on the analysis of the main content and the latest progress of background difference, frame difference and optical flow. Finally, presents the strengths and weaknesses and the technical difficulties faced, and points out directions of future research in this area.

Abstract: This paper proposes a method of segmentation of hand signal, based on the skin color model in YCbCr space and background subtraction under complex background. This paper discussed the reasonable threshold selection of Cb, Cr in the skin color model and the segmentation selection of skin area combining the background segmentation. Finally, an inequality of hands outline feature is proposed to complete the division processing of the palm part of the area of skin. Experiments show the accurate segmentation of gesture under a complex static background.

Abstract: The traditional background difference method is based on gray image. Some information is lost when color image is transformed into gray image. So it is difficult to discriminate different colors with similar gray values and easily disturbed by noise and shadows. In this paper, the background difference is based on RGB color model. It is proposed to use the average value of each pixel of the color image sequences to extract the background, and then use the three-dimensional color values of the current frame and background image to compute the difference to detect the moving objects. The proposed approach is simple and easy to implement. The experimental results show that it is more sensitive to colors and has higher accuracy and robustness than the traditional background difference method. Besides, it is more resistant to shadows.

Abstract: Against the poor accuracy of the vehicle counters extracted by existing vehicle detection technology, a motion vehicle detection method based on self-adaptive background subtraction with cumulative inter-frame difference is proposed in this paper. Cumulative inter-frame difference is used to subtract binary object mask. According to the binary object mask, in the area of moving objects the pixels of last background are used to modify the current background, otherwise the pixels of current image are used. The result of this operation is the current background. Then the background difference method is used to detect moving vehicles.

Abstract: Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. Experimental results show the algorithm used in this paper can detect the moving vehicles better than traditional moving vehicles detection algorithms.

Abstract: The Electronic Police System is an important trend of the development of the Intelligent Traffic System present. Aim at video images taken by the Electronic Police System, a kind of vehicle detection algorithm based on detecting area is proposed. This method combines background difference with edge difference and morphological steps to detect vehicles, and the accuracy of the algorithm is further improved by correcting the frame data stream. Experimental results demonstrate that the algorithm is effective and can meet the needs of real-time requirement.