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Human tracking in crowded scenes is a challenging problem because occlusion is frequently occurred. In this paper, we propose an online human tracking method which can handle occlusion effectively. Our method automatically changes a learning rate for updating tracking model according to the situation. If the tracking target is under occlusion, the learning rate decreases to reduce the influence of...
In this paper, an orbit control method for the rendezvous with the non-cooperative maneuvering target is proposed. In the near range guided phase of rendezvous and docking, the relative motion model is established firstly. Based on this relative motion model, an optimal control method before maneuvering and a sliding mode control method after maneuvering are introduced. This association method is...
In recent years, occluded target tracking has become a hot topic in the research field of machine vision. Mean shift(MS) and particle filter(PF) are two successful methods for target tracking. Both have respective advantages and weaknesses. MS algorithm is good at prompting tracking but sensitive to occlusion while PF algorithm is robust to occlusion but need extensive computation cost. In order to...
This paper proposes an efficient tracking method to handle the appearance of object. Distribution fields descriptor (DF) which allows the representation of uncertainty about the tracked object has been proved to be very robust to illumination changes, image noise and small misalignments. However, DF tracking is a generative model that does not utilize the background information, which limits its discriminative...
Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram...
The object tracking by using single feature is possible to generate errors and easy to lose the target if the illumination and object size scale are changed. We propose a particle-filter-object-tracking algorithm. The proposed algorithm is based on a covariance region descriptor (CRD). The CRD can fuse different features of a targeted object region while handling various complex backgrounds. Hence,...
Visual tracking is a fundamental research problem in computer vision field. In this paper, we propose an approach to incorporate visual prior into visual object tracking via deep neural network. Visual prior knowledge is expressed as the parameters of a stacked denoising autoencoder, which is trained from a large collection of natural images. By utilizing natural images, we can obtain generic image...
Robust scale calculation is a challenging problem in visual object tracking. Most state-of-the-art trackers fail to handle large scale variations in complex image sequences. This paper propose a novel approach for robust scale calculation in a tracking-by-detection framework. The proposed approach divides the target into four patches and computes the scale factor by finding the maximum response position...
In this paper an algorithm based on light radiation model and combined with the singularities of gradient descent and strengthening method to track the light sources is presented, which gives the accurate model for tracking light sources or secondary light sources. The entire model does not need any limitations for the light sources, which greatly enhanced the applicability of this model. The tracking...
Most traditional visual tracking algorithms ignore the importance of tracking failure detection which is helpful for occlusion handling. To address this problem, a robust visual tracking algorithm combining the tracking model and the failure detection strategy is proposed for the needs of practical applications. In order to tracking the position and scale of the target with high speed and accuracy,...
The multiple-model multi-Bernoulli (MM-MB) filter is a new attractive approach for estimating multiple maneuvering targets in the presence of clutter, missed detection and data association uncertainty. In this paper, we extend the Gaussian Mixture (GM) MM-MB filter to nonlinear models by using unscented transform techniques. Moreover, in order to improve the robustness and numerical stability of the...
The authors have investigated a construction methodology for a non-invasive ultrasound theragnostic system, called NIUTS, to track and follow moving body targets, such as tumors / stones located in moving organs (kidneys, livers, etc.), robustly and precisely, so as to compensate for nearly 90 percent of target motions in ultrasound images, while irradiating therapeutic ultrasound to focal lesions...
Aiming at classic Mean Shift based on tracking algorithm can not track target when the speed changed, the paper proposed an improved algorithm based on corrected background weighted histogram(CBWH), the algorithm used two iterations on target, first time used mean shift to iterate and calculated the similarity coefficient, second iteration used twice the current frame center minus the previous frame...
A particle filter tracking algorithm of multi-features fusion based on energy cumulant is proposed in this paper. This algorithm mainly focuses on the dim target tracking problem under complex background of infrared image sequence, and analyzes the different features of infrared dim targets. Since the particle filtering algorithm gives the advantage of multi-features fusion, this paper combines the...
A new anti-occlusion method for object tracking is presented to solve the problem that traditional visual tracking algorithms often deviate or lose the targets under occlusion. The motion position of blocked object can be obtained by the further iterative calculation of mean shift algorithm in the particle filter tracking results when the target is occluded, and the approximation and accuracy of tracking...
Subspace methods have attracted increasing attention for visual tracking. However, most previous work only aim to pursuit the subspace basis to represent appearances, thus cannot reveal the rich structure information in real world videos. This paper proposes a guided low-rank subspace learning framework to simultaneously extract the orthogonal subspace basis, the low-rank coefficients and the sparse...
In this paper, we present a robust and fast object tracking algorithm based on sub-region classifiers and compressive tracking. Compared with the original CT algorithm, the tracker can improve the robustness to occlusion, especially long-term occlusion. Firstly, the target region is divided into four sub-regions in a fixed mode. Then a simple but feasible classification and update strategy is used...
The problems of moving object detecting and tracking on UAV (Unmanned Aerial Vehicle) platforms in surveillance operations are addressed, and a robust tracking method for reliably detecting and tracking of foreground moving objects in a dynamically changing environment is proposed. Firstly, using the modified robust local feature detector SURF (Speeded Up Robust Features) which is linear ordering...
For the traditional problems of template drift and the low efficiency of algorithms in video image target tracking applications. A new template tracking algorithms based on active drift correction is proposed. The algorithm incorporates a drift correction term and a target tracking term. By adding a drift correction term into the traditional affine image alignment algorithms and update the template...
We propose a novel robust probability hypothesis density (PHD) filter for multiple target tracking in an enclosed environment, where a one-class support vector machine (OCSVM) is used in the update step for combining different human features to mitigate the effect of measurement noise on the calculation of particle weights. A Student's-t distribution is employed to improve the robustness of the filters...
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