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Improved the traditional L-K algorithm by lifting the wavelet multi-resolution algorithm, and the tracking speed of system was greatly enhanced while combined with SURF matching algorithm. On the basis of detecting feature points, reduced the probability of the exterior points. Tracking local feature points by multi resolution wavelet Pyramid optical flow algorithm solved the problems of object deformation,...
Currently, Compressive Tracking (CT) method has drawn great attention because of its high efficiency. However, it cannot well deal with some appearance variations due to its limitations of feature expression and it only uses a fixed parameter to update the appearance model. In order to handle such matters, we propose an adaptive CT method that combines the predicted target position with CT based on...
In order to handle occlusion and illumination change in video object tracking, an algorithm named compressive object tracking based on weighted multiple instance learning (COTWMIL) is proposed. Each sample is descripted in a multi-scale fashion with rectangle filterbank, and then the dimension of extracted feature is reduced by compressive sampling. Specifically, object region is first labeled manually...
In this paper, we exploit deep convolutional features for object appearance modeling and propose a simple while effective deep discriminative model (DDM) for visual tracking. The proposed DDM takes as input the deep features and outputs an object-background confidence map. Considering that both spatial information from lower convolutional layers and semantic information from higher layers benefit...
In this paper, real-time recognition and tracking of multiple similar targets at 6-DOF motion is studied. A real-time multi-target recognition algorithm is proposed and implemented based on Marker to solve the difficult problem of distinguishing multiple similar targets. Because the lighting conditions of markers at 6-DOF motion are widely changeable, existing marker recognition algorithms are sensitive...
Moving multi-targets tracking under variant illumination in a large range is still not highly robust, especially in changeable scenes of video sequence. In order to solve this problem in these certain scenes, the illumination model is established based on the sunlight and sky light sources. A multiple target tracking method based on multiple features with depth information in the regions is proposed...
Object tracking task faces serious challenges when a desired target is in complex circumstances such as obstacle occlusion, pose variation, illumination change and motion blur. Despite lots of excellent tracking algorithms have been proposed, many issues remain to be addressed. In this paper, we propose a novel object tracking algorithm to combine both Enhanced Perception Hash and Coarse-to-fine Sliding...
Humans have the capability to quickly prioritize external visual stimuli and localize their most interest in a scene. Inspired by this mechanism, we propose a robust object tracking algorithm based on visual attention. We fuse motion feature and color feature to estimate the target state under the guidance of saliency map. Principal Component Analysis method is used to compute saliency feature based...
Pose tracking is a fundamental technology needs to be solved in on-orbit service. Visual pose tracking provides an inexpensive solution while it remains a large obstacle to track satellites under space illumination changing condition. This paper combines point feature with edge feature to track a model-known satellite. It makes use of the distinctiveness of point feature and robustness of edge feature,...
This paper aims at presenting a simple and efficient single target tracking approach, which is suitable for the applications for modern smart glasses. Specifically, a novel set-to-set similarity measurement is proposed, which takes the critical appearance cues, the contextual structure of the points inside the set and the points' coordinate related to the bounding box into consideration jointly. In...
Object recognition and tracking are important and challenging tasks in many computer vision applications. Difficulties in object recognition arise due to occlusion, clutter and geometric transformations present between pair of images or frames. Challenges in tracking include ability to deal with abrupt object motion, nonrigid object structures, change in appearance patterns of scene and object, occlusions...
An existing deep learning architecture has been adapted to solve the detection problem in camera-based tracking for augmented reality (AR). A known target, in this case a planar object, is rendered under various viewing conditions including varying orientation, scale, illumination and sensor noise. The resulting corpus is used to train a convolutional neural network to match given patches in an incoming...
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore,...
Inspired by the recent image feature learning work, we propose a novel key point detection approach for object tracking. Our approach can select mid-level interest key points by max pooling over the local descriptor responses from a set of filters. Linear filters are first learned from targets in first frames. Then max pooling is performed over data driven spatial supporting field to detect discriminant...
In this paper, we present a simple yet effective visual tracking algorithm with an appearance model based on 2D discrete cosine transform (2D-DCT) representations. The DCT has the properties of decorrelation and energy compaction, and is robust against geometry and illumination changes. Hence, it is suitable for appearance modeling and the features of our appearance model are extracted from an optimized...
As for the problems of target blocking and illumination changes in motive target tracking, a particle filtering algorithm based on compressive sense is proposed in this paper. We add the extracted features based on compressive sense of the improved CT algorithm into the framework of particle filtering tracking and judge the credibility of extracted features, as well as the color features of original...
An aerial surveillance method is proposed for a predefined single object tracking. The algorithm takes advantage of template matching to be able to track the selected object in video sequences. The selected templates are chosen from the images by utilizing gradient operation on the Gabor Wavelet representations. The algorithm achieves the tracking task even in the cases where i) the observer is moving,...
The classical mean shift algorithm is easy to pass into local maxima, which is caused by the lack of appropriate target model updating mechanism. In this paper, a SIFT-based mean shift algorithm is proposed, which can be used for continuous vehicle tracking in complex situations, such as the shape and the illumination of the vehicle object change. In our algorithm, the mean shift algorithm is utilized...
This paper proposes a novel tracking algorithm to cope with the appearance variations of vehicle in the natural environments. The algorithm utilizes the discriminative features named pixel-pair features for estimating the similarity between the template image and candidate matching images. Pixel-pair features have been proved to be robust for illumination changes and partial occlusions of the training...
In this paper, we propose an object tracking algorithm Based on particle filter using integral channel features. Integral channel features are the extension of features which can be computed using the integral image of multiple image channels. They combine diversity of information and high computational efficiency. In this algorithm, two kinds of integral channel features (the gray and the gradient...
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