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Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature...
Human face detection is a key technology in face information processing, the speed of which is very important during real-time face detection for input images or input video sequences. This paper presents a novel face window searching algorithm based on evolutionary agent when detecting faces in gray-scale images. It can quickly And the candidate face windows through the evolutionary computation of...
This paper proposes a novel vehicle detecting approach for surveillance scenes with single stationary camera. Difference accumulative based background modeling method is used for background modeling. Background subtraction operation is used for detecting moving vehicles and Otsu method is used to threshold the background difference image. Subtractive clustering algorithm is applied for vehicle locating...
This paper presents a novel scheme for dynamically recovering a background image from consecutive frames of a video sequence based on spatial and temporal continuities. The proposed background subtraction algorithm applies a boundary-level spatial continuity constraint in order to detect and correct ghosting, which corresponds to incorrectly classified foreground regions due to fast moving objects...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
In the existed background modeling methods, specified threshold parameters need to be given by user when the probability map is established in order to obtain the optimal segmentation results. In this paper, a new method is proposed, in which the feature intensity function of video adjacent relationship is calculated adaptively according to video features. It will avoid the drawbacks that video adjacent...
Detecting fiducial points successfully in facial images or video sequences can play an important role in numerous facial image interpretation tasks such as face detection and identification, facial expression recognition, emotion recognition, and face image database management. In this paper we propose an automatic and robust method of facial fiducial point's detection for facial expressions analysis...
A probabilistic Bayesian belief network (BBN) based framework is proposed for semantic analysis and summarization of video using event detection. Our approach is customized for soccer but can be applied to other types of sports video sequences. We extract excitement clips from soccer sports video sequences that are comprised of multiple subclips corresponding to the events such as replay, field-view,...
This paper presents a novel scheme for extracting a still background occluded by a number of foreground objects, moving in different directions and velocities in a video sequence, such that every background pixel is exposed in at least one of the frames. Each identified foreground object is decomposed into blocks. The proposed scheme is able to efficiently estimate, for each foreground block, a source...
In this paper we propose a novel fast fuzzy classifier able to find regular and low distorted near regular texture taking into account the constraints of video stabilization applications. Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In presence of regular or near regular texture, video stabilization approaches typically...
In this paper we present a slice group based unequal error protection (UEP) scheme for video transmission over error- prone networks. We propose a method to assign macroblock to slice groups based on a variation of H.264/AVC dispersed FMO mode and k-means clustering algorithm. In addition, a Converged Motion Estimation (CME) is proposed to further improve our UEP scheme. The idea behind the CME is...
Very low bit-rate video coding algorithms using content-based generated patterns to segment out moving regions at macroblock level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as extra mode. This content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving...
Based on the fact that most of the algorithms assume that the camera is fixed and the changing background is learned in the training period, a robust algorithm is proposed for complex background where a shaking camera, changing background and shadows are presented. It combines a new improved mixture of Gaussians model and a square neighborhood matching algorithm to eliminate shadows and reduce false...
In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult problem because of the double constraint on the available time and the computational cost of robust object extraction algorithms. This paper builds upon former work on combining the strong theoretical foundations of clustering...
In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used...
New hierarchical method for detecting and classifying shot boundaries in video sequences using information theoretic classification (ITC) rule is proposed. ITC relies on likelihood of class label transmission of a data point to data points in its vicinity. ITC focuses on maximizing the global transmission of true class labels and classifying the frames into two classes of cuts and non-cuts. Applying...
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