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Action recognition has been an active research area in computer vision community during the recent years. However, it is still a challenging task due to the difficulties mainly resulted from the background clutter, illumination changes, large intra-class variation and noise. In this paper, we aim to develop an action recognition approach by navigating focus of attention (action region) with saliency...
Aim of this paper is to propose a solution to the correspondence problem in multi-camera systems. In these systems, two or more cameras are used to record the same scene from different view points. In this way it is possible to face the problem of occlusions in crowding scenes. In this work an object level motion detection algorithm is used and it is applied to the videos sampled by two cameras. The...
The performance of visual tracking mainly depends on observation models and search methods. To stabilize the tracker, an algorithm for estimating velocity feature based on principal component analysis (PCA) is proposed. The proposed algorithm calculates the velocity feature using PCA by the states of the object in the previous k frames. In addition, integrating color and motion cues for visual tracking...
A new scheme for back-projection of weights for mean shift based object tracking is proposed. Weights are calculated based on relative counts of histogram bins for each feature used in similarity assessment. A fusion scheme is proposed to combine the back-projected weights from different features, such that the dissimilarities between the object being tracked and the background are boosted. A mechanism...
The paper presents a new tracking scheme based on the object-strips color (OSC) feature. Firstly, the images captured by the camera are transformed into a format which is suitable for object tracking. Secondly, background subtraction method is used to detect the moving object. Then the OSC feature is represented by dividing the detected object into several strips and integrating the mean hue of each...
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. New spatio-temporal features, color features and localization features are proposed...
In film-making, the distance from the camera to the subject greatly affects the narrative power of a shot. By the alternate use of Long shots, Medium and Close-ups the director is able to provide emphasis on key passages of the filmed scene. In this work we investigate four different inherent characteristics of single shots which contain indirect information about scene depth, without the need to...
Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly - even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce...
A supervised softbot utilized for analyzing, segmenting, and properly classifying video clips pertaining to a wide variety of sporting events is presented. First, selected action scenes (i.e., training sequences) of a given sporting event are automatically segmented into real-world objects representing the participants of the activity. These objects correspond to the players, the playing field (or...
We proposed a method for automatic detection and tracking of moving object employing a particle filter in conjunction with a color feature method. The particle filtering is used because it is robust for non-linear and non-Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present on the image. A histogram-based framework is used to describe the color feature...
In this paper, an object detection and tracking algorithm is proposed. At the object detection stage, the spatial features of object are extracted by wavelet transform, according to frame difference, the moving target is determined. In order to effectively utilize the temporal motion information, Markov random field prior probability model and the observation field model are established, taking advantage...
Object tracking algorithm using modified Particle filter in low frame rate (LFR) video is proposed in this paper, which the object moving significantly and randomly between consecutive frames in the low frame rate situation. Traditionally, Particle filtering use motion transitions to model the movement of the target. However, in object tracking with low frame rate sequences, it is very difficult to...
An approach is proposed for abnormal sections detection in video sequences. In this approach, firstly the histogram is selected to describe the color change in the section, and then the histograms of the frames selected from the section compose the histogram matrix. In order to improve the process efficiency, the principal components analysis (PCA) is used to reduce dimensions of the histogram matrix...
This paper presents visual features for tracking of moving object in video sequences using Mean Shift algorithm. The features used in this paper are color, edge and texture. Mean shift Algorithm is expanded based on mentioned multiple features, which are described with highly nonlinear models. In the proposed method, firstly all the features is extracted from first frame and the histogram of each...
In this paper, a new method for detecting shot boundaries in video sequences using a late fusion technique is proposed. The method uses color histogram as the feature, and processes each bin separately for detecting shot boundaries. The decisions from individual bins are combined later for hypothesizing the presence of shot boundaries. The method provides a certain degree of robustness against illumination...
Easily falling into local extremum, plateaus, and fast moving targets could't tracked, which are main handicap to mean shift application, especially in those cases to track the multi-articulated human body fine features. Based on the analysis of the causes of the mean shift, particle swarm optimization is introduced into the mean shift to solve this problem in this paper. Here, the mode estimation...
This paper proposes a GPU based algorithm for extracting moving objects in real time. The whole process of the proposed approach is handled on GPU. GPU is used for acceleration and the proposed approach increases processing speed dramatically. The method uses a* component and b* component of CIELAB color space without extracting shadow areas as moving objects. It is robust to intensity changes because...
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