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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...
In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision...
Motion estimation is one of the basic problems in digital video processing; it is significant in the applications of video image compression, registration, mosaic, and target detection, and so on. In the base of discussing basic phase correlation algorithm, a method based on kernel regression for constructing two-dimensional circular symmetry window function has been introduced, and the improved scheme...
We address the problem of license plate detection in video surveillance systems. The Adaboost based approach, known for relative ease of implementation, makes use of discriminative features such as edges or Haar-like features. In this paper, we propose a novel detection algorithm based on local structure patterns for license plate detection. The proposed algorithm includes post-processing methods...
We describe a real-time multiple face-tracking algorithm under highly occlusion. In order to resolve the occlusion and temporal lost problem, a robust data association + filtering procedure is proposed. The mechanism combines the census transform based block-by-block strategy to infer the occlusion state via concerning observation changes of two faces. And a robust and straightforward filtering approach...
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...
This paper reviews and evaluates performance of few common background subtraction algorithms which are median-based, Gaussian-based and Kernel density-based approaches. These algorithms are tested using four sets of image sequences contributed by Wallflower datasets. They are the image sequences of different challenging environments that may reflect the real scenario in video surveillances. The performances...
During the last years, Automatic video analysis has become a very important research for video management, such as video index and video retrieval. The application domains are disparate, ranging from video surveillance to automatic video annotation for sport videos or TV shots. Whatever the application field, most of the works in video analysis are based on two main approaches: the former based on...
Human figure segmentation (HFS) is at the very core of many image and video processing tasks. Many solutions have been proposed for the separation of objects, or more specifically human figures, from image background in a video scene. Unfortunately, these solutions do not provide tight human segmentation in the most general conditions, so only a coarse segmentation of human figures can be assumed...
Human detection has always been an important part of computer vision but many implementations lack the real-time performance that real world applications require. This paper presents a real-time implementation of human detection in video using the state-of-the-art histograms of oriented gradients method. Each image in the video sequence is tested at multiple scales using a sliding window. Histograms...
We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color...
In this paper we implement a wireless vision based object tracking system with wireless surveillance camera which uses a novel color based object tracking algorithm designed to work on any non-ideal environment. The implementation of the kernel-based tracking of moving video objects based on the CAMSHIFT algorithm is presented. We show that the algorithm performs exceptionally well on moving objects...
In this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly...
We present an algorithm for detecting human actions based upon a single given video example of such actions. The proposed method is unsupervised, does not require learning, segmentation, or motion estimation. The novel features employed in our method are based on space-time locally adaptive regression kernels. Our method is based on the dense computation of so-called space-time local regression kernels...
In this paper, we propose a method for fast pedestrian detection in images/videos. Multi-scale orientated (MSO) features are proposed to represent coarse pedestrian contour, on which Adaboost classifiers are trained for pedestrian coarse location. In the fine detection, histogram of oriented gradient (HOG) features and SVM classifiers are employed to precisely classify pedestrians and non-pedestrians...
An improved, intelligent pedestrian counting system, using images obtained from a single video camera, is described in this paper. This system is capable of detecting and counting a group of pedestrians in the region of interest. Groups can be extracted by using the image processing method, and a kernel-induced probabilistic neural network (KPNN) employed to perform the classification, and estimate...
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...
An object tracking algorithm that uses the flexible kernels based on the normalized metric dalpha distance transform for the mean shift procedure is proposed and tested. This replaces the more usual Epanechnikov kernel (E-kernel), improving target representation and localization without increasing the processing time, minimizing the similarity measure using the Bhattacharya coefficient. The target...
This paper proposes a method of adaptive kernel density estimation (KDE) for motion detection. The method selects an adaptive threshold by analyzing probability histogram, which is suitable for different scenes and different moving objects. Then a mechanism of updating background using probability is also provided. It can get relative good background and is useful for motion detection. Moreover it...
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...
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