An association algorithm between targets and trajectories is put forward in multi-target tracking. Target tracking is based
on multi-feature camshift and particle filter in each camera. An optimal search is carried out near dynamic transfer center
by camshift, which is helpful to let each particle reach a stable position. Then a new sequence with high weights is
obtained by resampling after it is to enhance high weight particles. Initial and end points of target trajectories are judged
by FOV (field of view) boundaries. Finally, FCM algorithm is used to do data association between measurement points
and tracks. Experimental results indicate that the algorithm is robust to occlusion.
A feature matching method is proposed based on Maximally Stable Extremal Regions (MSER) and Scale Invariant
Feature Transform (SIFT) to solve the problem of the same target matching in multiple cameras. Target foreground is
extracted by using frame difference twice and bounding box which is regarded as target regions is calculated. Extremal
regions are got by MSER. After fitted into elliptical regions, those regions will be normalized into unity circles and
represented with SIFT descriptors. Initial matching is obtained from the ratio of the closest distance to second distance
less than some threshold and outlier points are eliminated in terms of RANSAC. Experimental results indicate the
method can reduce computational complexity effectively and is also adapt to affine transformation, rotation, scale and
illumination.
The main problem of now visual tracking algorithm is that the algorithm is lack of robustness, precision and speed. This
paper gives a visual tracking method based on dynamic object features extracting. First extract object features according
to the value of current frame image and build feature base. Then evaluate the recognition ability of every feature in
feature base using fisher criteria and select high-recognition features to generate object feature set. Dynamic adjust the
feature vectors of feature set according to the changes of environment object lie in. finally process visual tracking
adopting particle filter method using feature vectors of feature set. Experiments have proved that this method can
improve the tracking speed while assure tracking accuracy when lighting environment that moving objects lie in
changes.
Multi-projector virtual environment based on PC cluster has characteristics of low cost, high resolution and widely visual
angle, which has become a research hotspot in Virtual Reality application. Geometric distortion calibration and seamless
splicing is key problems in multi-projector display. The paper does research on geometry calibration method and edge
blending. It proposes an automatic calibration preprocessing algorithm based on a camera, which projects images to the
regions expected in terms of the relation between a plane surface and a curved surface and texture mapping method. In
addition, overlap regions, which bring about intensity imbalance regions, may be adjusted by an edge blending function.
Implementation indicates that the approach can accomplish geometry calibration and edge blending on an annular
screen.
Grid computing has developed rapidly with the development of network technology and it can solve the problem of
large-scale complex computing by sharing large-scale computing resource. In grid environment, we can realize a
distributed and load balance intrusion detection system. This paper first discusses the security mechanism in grid
computing and the function of PKI/CA in the grid security system, then gives the application of grid computing character
in the distributed intrusion detection system (IDS) based on Artificial Immune System. Finally, it gives a distributed
intrusion detection system based on grid security system that can reduce the processing delay and assure the detection
rates.
Computational Intelligence is the theory and method solving problems by simulating the intelligence of human using computer and it is the development of Artificial Intelligence. Fuzzy Technique is one of the most important theories of computational Intelligence. Genetic Fuzzy Technique and Neuro-Fuzzy Technique are the combination of Fuzzy Technique and novel techniques. This paper gives a distributed intrusion detection system based on fuzzy rules that has the characters of distributed parallel processing, self-organization, self-learning and self-adaptation by the using of Neuro-Fuzzy Technique and Genetic Fuzzy Technique. Specially, fuzzy decision technique can be used to reduce false detection. The results of the simulation experiment show that this intrusion detection system model has the characteristics of distributed, error tolerance, dynamic learning, and adaptation. It solves the problem of low identifying rate to new attacks and hidden attacks. The false detection rate is low. This approach is efficient to the distributed intrusion detection.
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