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Motion detection is of paramount importance in video surveillance systems. In this paper, a novel algorithm is proposed to extract the exact boundaries of moving objects in video frames. Using the concepts of Cross-Correlation and Edge Detection, we combine two well-known motion detection methods to extract the moving regions more accurately. Also, we modify these two methods in terms of accuracy...
Background subtraction is a traditional method for detecting objects in stationary background. However, this traditional method is difficult to detect objects accurately in the real world, because the background is usually cluttered and not completely static. In this paper, we propose an object detection approach using Ant Colony System (ACS) in a MAP-MRF framework. For object segmentation, a MAP-MRF...
This paper proposes a novel visual object tracking scheme, exploiting both local point feature correspondences and global object appearance using the anisotropic mean shift tracker. Using a RANSAC cost function incorporating the mean shift motion estimate, motion smoothness and complexity terms, an optimal feature point set for motion estimation is found even when a high proportion of outliers is...
Blur is caused by a pixel receiving light from multiple scene points, and in many cases, such as object motion, the induced blur varies spatially across the image plane. However, the seemingly straight-forward task of estimating spatially-varying blur from a single image has proved hard to accomplish reliably. This work considers such blur and makes two contributions: a local blur cue that measures...
In this paper, we improve the real-time object tracking algorithm of Yang [1] which uses a symmetric similarity function between spatially smoothed kernel-density estimates of the model and the target distributions. This spatial smoothed process applied on the centre points of the probability density functions increases not only computational complexity but also noise sensitivity. After reducing background...
Kernel tracking of density-based appearance models is implemented in this paper for real-time object tracking applications. First a ROI, i.e., the region of interest is selected in real-time to create a model. Then the matching and locating of the search object is achieved by using mean-shift algorithm. Experimental results show that this method can find perform object tracking with adaptation to...
In this paper, a method for real-time tracking of moving targets is proposed. The particle filter and mean shift technical for color-based tracking is used. The traditional tracker always focuses on how to track with the object robustly in a short period of time. Most of them modify the model after the tracking is finished in current frame. But in long time tracking, the object model is changing continuously...
Kernel-based tracker shows robust performances in various object tracking technologies. Due to its robustness and accuracy, kernel-based tracker using mean-shift algorithm is regarded as one of the best ways to apply in object tracking technology in computer vision fields. However, it fails tracking when faced with a speedy object moving beyond its window size within one image frame interval time...
The paper studies new constraints that characterize a 3D-motion field as observed from the relative motion of a camera. Such constraints are derived from the relative change in size of observed local image regions over time. To consider the image distortions that arise in a projective camera, a modified affine shape adaptation scheme is proposed for the case of blob detection, with an emphasis on...
Traditional mean shift tracking algorithm set weight value of pixels according to the distance between pixel and center of model. But it is obviously unreasonable during the tracking of asymmetric or non-rigid object, such as human. In this paper, a novel adaptive weight values updating mean shift tracking algorithm is proposed, weight value of every pixel is updated according to variation of motion...
Various powerful people detection methods exist. Surprisingly, most approaches rely on static image features only despite the obvious potential of motion information for people detection. This paper systematically evaluates different features and classifiers in a sliding-window framework. First, our experiments indicate that incorporating motion information improves detection performance significantly...
Spatio-temporal salient features are widely being used for compact representation of objects and motions in video, especially for event and action recognition. The existing feature extraction methods have two main problems: First, they work in batch mode and mostly use Gaussian (linear) scale-space filtering for multi-scale feature extraction. This linear filtering causes the blurring of the edges...
Background modeling is an essential and important part of many high-level video processing applications. Recently, the Support Vector Data Description (SVDD) has been introduced for novelty detection when only one class of data is available, i.e. background pixels. This paper proposes a method to efficiently train an SVDD and compares the performance of this training algorithm with the traditional...
Background subtraction is a popular moving object detection technique, but its performance is dependent of the accuracy of background model. In this paper, the theory of sequential kernel density approximation is first introduced to background modeling. To this end, a novel background subtraction method for moving object detection is proposed. Various real video sequences have been used to test this...
Fast and reliable detection of moving objects is one of the important requirements for video surveillance systems. Mean shift based non-parametric background modeling supports more sensitive and robust detection in dynamic outdoor scenes. However it is prohibitive for real-time applications such as video surveillance. This paper aims to deal with the limitation of high computational complexity. Firstly,...
A new method is proposed to improve background modeling speed. First, the pixels in current frame are classified into two classes according to average background to reduce the computing load. Second, different models for instance kernel or GMM based algorithm are used necessarily to deal with 'dead lock' of scene. Third, a kernel density estimation based on neighbor correlation is used to decrease...
Robust detection of moving objects in complex and dynamic scenes is one of the most challenging issues in computer vision. In this paper, we present an approach to segmenting moving objects with nonparametric estimated local kernel histogram (ELKH) in dynamic scenes. By using the correlation and texture of spatially proximal pixels, local kernel histogram background model is constructed. Then probability...
Traditional mean shift algorithm requires a symmetrical kernel, such as a circle or an ellipse, and assumes the kernel represents the object shape. Because the symmetrical kernel always contains some background regions, the performance of moving object tracking is dramatically affected when background is complex and changes greatly. To address above issue, this paper proposes an improved mean shift...
A new approach to adapt the kernel scale and orientation in real-time tracking is proposed. The iterative procedure, mean shift, is the key point to find the most credible target location. Though it performs well in some bad conditions, such as camera motion, partial occlusions, and background clutters, it has limited performance on tracking the object with the changing size. In this paper, the adaptive...
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