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This paper propose a method for automatic vehicle detection in QuickBird images of small highways, with relatively low traffic density, frequent occurrence of tree shadows, and changes in illumination conditions. The vehicle detection is based on an elliptical Laplacian of Gaussian scale space methodology, where the vehicle locations are detected at local extrema in the image response to convolution...
In view of the Kirsch edge detection algorithm's shortage, a method of improved Kirsch human face edge detection was proposed. First, this method smoothed the original image with Gaussian filter, calculated its gradient image. Then, this method calculated the gradient image using the improved Kirsch algorithm, and extract edge according T1 and T2 two. Computer simulation experiments indicated that...
Background Modeling is the normal method on the moving objects detection, it plays a key role in the moving objects detection and tracking, the Gaussian mixture model is one of the most successful methods on the detection. But it converges slowly in the complex scene. This paper proposes a new method named “additive increase” and the “additive decrease” to adjust the weight of the matched distributions...
Image restoration is the process of recovering the original image from the degraded image. Aspire of the project is to restore the blurred/degraded images using Blind Deconvolution algorithm. The fundamental task of Image deblurring is to de-convolute the degraded image with the PSF that exactly describe the distortion. Firstly, the original image is degraded using the Degradation Model. It can be...
This paper presents a real-time background subtraction method which handles illumination changes and dynamic backgrounds such as flapping flags and waving trees. Previous approaches based on Gaussian mixture models usually generates models pixelwise, which makes it difficult to operate in realtime due to computational complexity. Moreover, pixelwise models tend to fail in sudden illumination changes...
A self-adaptive canny operator was developed to detect edges of growing citrus images. RGB color images were obtained and linear transformed into R-B chromatic aberration space at first. In R-B space, width of Gaussian filter fast calculated using integral images and the high and low threshold values obtained by OTSU algorithm were extracted to improve automatic edge detection. It is shown that the...
In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background...
Detecting people carrying objects is a commonly formulated problem as a first step to monitor interactions between people and objects. Recent work relies on a precise foreground object segmentation, which is often difficult to achieve in video surveillance sequences due to a bad contrast of the foreground objects with the scene background, abrupt changing light conditions and small camera vibrations...
This work studies eye color as a soft biometric trait and provides a novel insight about the influence of pertinent factors in this context, like color spaces, illumination and presence of glasses. A motivation for the paper is the fact that the human iris color is an essential facial trait for Caucasians, which can be employed in iris pattern recognition systems for pruning the search or in soft...
Detecting static objects in video sequences has a high relevance in many surveillance scenarios like airports and railwaystations. In this paper we propose a system for the detection of static objects in crowded scenes that, based on the detection of two background models learning at different rates, classifies pixels with the help of a finite-state machine. The background is modelled by two mixtures...
This paper presents the challenging issue of target detecting and pose estimation in Forward Looking Infrared (FLIR) sequences. Target detecting and pose estimation are formulated as one process. Specifically, a new mixture of Gaussians model is proposed to detect target appearances. A probability based method is used to estimate the target position and size, where the detected appearance plays an...
In order to eliminate vehicle shadow in moving vehicle detection, a novel vehicle shadow elimination approach is presented based on mark growing of multi-feature fusion. The selection of mark points and the establishment of growing rules are critical. Firstly, background model is obtained by mixture Gaussian method. Then gradient difference of foreground and background is calculated. Morphological...
In this paper, under the non-local means framework, we propose a non-local bilateral filter algorithm for image denoising based on the neighborhoods' gray value and the corresponding neighborhoods' Gaussian curvature. We also adopt a new method to provide the optimum denoising parameter h based on the discrete wavelet transform and the smoothing spline estimation. Meanwhile the generalized cross-validation...
This paper presents a new scheme for detecting humans' falls in highly dynamic house environments. The scheme distinguishes falls from other humans' activities, like sitting, walking, lying, under (a) sudden and abrupt illumination changes (b) non-periodic/significant motions in the background (chairs, curtains, tables), (c) humans' movements towards all possible directions across camera. In particular,...
This paper presents a background modeling algorithm and a foreground detecting method which is robust against illumination change, providing a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online Expectation Maximization algorithm which is developed from the basic batch edition to update the mixture models in real time. Then...
For any autonomous system it is very important to acquire the knowledge of the surrounding environment. Images and videos acquired by the vision based sensors can provide meaningful information about the environment, which can be very useful for the navigation of autonomous system like mobile robots. To extract road information from image frames for navigation purpose they have to be classified. Classification...
In this paper an adaptive Gaussian mixture model is introduced firstly to remove the shadow of regions of interest in the detection of moving human body from current video sequences. Then use a proposed method of obtaining ROI. From the view of the tracking effect, it can be concluded that this method of removal shadow of regions of interest can improve the precision rate of segment of moving people...
The improved moving object detection and shadow removing algorithms for video surveillance are presented in this paper. The proposed algorithm processes two foregrounds performed by improved GMM and chromaticity-gradient background subtraction methods. The proposed algorithm improves the classic Gaussian Mixture Model to remove some unfavorable influences, such as sudden and gradual illumination changes,...
The background identification methods are used in many fields like video surveillance and traffic monitoring. In this paper we propose a hardware implementation of the Gaussian Mixture Model algorithm able to perform background identification on HD images. The proposed circuit is based on the OpenCV implementation, particularly suited to improve the initial background learning phase. Bit-width has...
Image averaging can be performed very efficiently using either separable moving average filters or by using summed area tables, also known as integral images. Both these methods allow averaging to be performed at a small fixed cost per pixel, independent of the averaging filter size. Repeated filtering with averaging filters can be used to approximate Gaussian filtering. Thus a good approximation...
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