With the development of the application of visual tracking technology, the performance of visual tracking algorithm is important. Due
to many kinds of voice, robust of tracking algorithm is bad. To improve identification rate and track rate for quickly moving target,
expand tracking scope and lower sensitivity to illumination varying, an active visual tracking system based on illumination invariants
is proposed. Camera motion pre-control method based on particle filter pre-location is used to improve activity and accuracy of track
for quickly moving target by forecasting target position and control camera joints of Tilt, Pan and zoom. Pre-location method using
particle sample filter according to illumination invariants of target is used to reduce the affect of varying illumination during tracking
moving target and to improve algorithm robust. Experiments in intelligent space show that the robust to illumination vary is improved
and the accuracy is improved by actively adjust PTZ parameters.
So far most research of human behavior recognition focus on simple individual behavior, such as wave, crouch, jump
and bend. This paper will focus on abnormal behavior with objects carrying in power generation. Such as using mobile
communication device in main control room, taking helmet off during working and lying down in high place. Taking
account of the color and shape are fixed, we adopted edge detecting by color tracking to recognize object in worker. This
paper introduces a method, which using geometric character of skeleton and its angle to express sequence of
three-dimensional human behavior data. Then adopting Semi-join critical step Hidden Markov Model, weighing
probability of critical steps' output to reduce the computational complexity. Training model for every behavior, mean
while select some skeleton frames from 3D behavior sample to form a critical step set. This set is a bridge linking 2D
observation behavior with 3D human joints feature. The 3D reconstruction is not required during the 2D behavior
recognition phase. In the beginning of recognition progress, finding the best match for every frame of 2D observed
sample in 3D skeleton set. After that, 2D observed skeleton frames sample will be identified as a specifically 3D
behavior by behavior-classifier. The effectiveness of the proposed algorithm is demonstrated with experiments in similar
power generation environment.
To speed algorithm convergence and avoid early-maturing, the theory of Uniform Design Sampling (UDS) is used to
redesign the crossover operation of Genetic Algorithm and to improve the similarity of cyber-chromosome which is
correlated with Detector Redundancy. A new detector prioritization scheme is built on the basis of the combination of
partial searching strategy and a new method to evaluate the data of redundancy. Simulation experiment demonstrates that
this scheme maintains the variety, efficiency and sufficiency of the detector. Our scheme has a better performance in
searching velocity, global optimal ability. Detection rate is increased and false alarm rate is decreased to a certain degree.
To improve detecting rates and reduce false detection of distributed network intrusion detection system, and to improve
parallel processing ability of distributed intrusion detection system,co-evaluation computation-based distributed intrusion
detection system is proposed. Optimized immune detecting method is used to reduce redundancy of detector. Multiagents
evaluation computation is used to enhance self-learning ability and self-adaptation ability of network intrusion
detection. Co-evaluation technology is used to speed co-evaluating of multi-agents in network intrusion detection system
and then improve evaluating ability of distributed system. Experiments verify the validity of the method.
To improve the identification rate and tracking rate for quickly moving target, expand tracking scope and
lower the sensitivity to illumination varying, an active visual tracking system self-adapting to illumination based on
particle filter pre-location is proposed. The algorithm of object pre-location based on particle filter is used to realize realtime
tracking to moving target by forecasting its location and control camera joints of Tilt and Pan. The method resetting
system is used to improve accuracy of system. Brightness histogram equalization method is used to reduce the affect of
illuminating varying in pre-location algorithm. Experiments and property analysis show that the real-time and accuracy
are greatly improved.
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
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
The traditional intrusion detection systems mostly adopt the analysis engine of the concentrating type, so the misinformation rate is higher and lack of self-adaptability, which is already difficult to meet increasing extensive security demand of the distributed network environment. An immunity-based model combining immune theory, data mining and data fusion technique for dynamic distributed intrusion detection is proposed in this paper. This system presents the method of establishing and evolving the set of early gene, and defines the sets of Self, Nonself and Immunity cells. Moreover, a detailed description is given to the architecture and work mechanism of the model, and the characters of the model are analyzed.
Antibody had a detecting effect in immune system. Simulating the generating and evolution and working process of the antibody in immune system is the key to build an immune-based intrusion detection system (IDS). This paper proposes a clone selection immune algorithm based on T-cell immunity. In this algorithm we adopt novel genotype and phenotype representations integrated with matching rule, which can show flexibly the 'or' relation between the rules for classifying. Besides, it makes generating detector more effective by introducing negative selection operator.
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
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.
Traditional IDS (Intrusion Detection System) performs detection by matching the sample pattern with the intrusion pattern that has been defined, as a result the IDS loses the diversity and the self-adaptation and can not detect the variation intrusion and the unknown intrusion. This paper gives a distributed intrusion detection approach based on the Artificial Immune System. It defines the Self, Nonself and immune cell and builds an intrusion detection model composed of memory cell, mature cell and immature cell and also gives the environment definition, matching rule, training detection system, immune regulation and memory, monitor generation and so on. The result of the experiment show that this intrusion detection system model has the characters of distributed, error tolerance, dynamic learning, adaptation and this approach is efficient to the network intrusion detection.