Face detection algorithm based on hybrid Monte Carlo method and Bayesian support vector machine

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

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Extension of the hybrid Monte Carlo method for boundary-value problems

SIMULATION ◽

10.1177/003754977302100605 ◽

1973 ◽

Vol 21 (6) ◽

pp. 177-179 ◽

Cited By ~ 1

Author(s):

Ludwig Fahrmeir

Keyword(s):

Monte Carlo ◽

Monte Carlo Method ◽

Boundary Value Problems ◽

Boundary Value ◽

Hybrid Monte Carlo

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Recognition of Human Emotion Detection and Annotation, using Local Descriptor and Support Vector Machine

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7201.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2838-2843

Keyword(s):

Support Vector Machine ◽

Object Detection ◽

Dynamic Environment ◽

Detection Algorithm ◽

Research Area ◽

Multimedia Data ◽

Video Annotation ◽

Support Vector ◽

People Detection ◽

Local Descriptor

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.

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CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

NED University Journal of Research ◽

10.35453/nedjr-ascn-2019-0100 ◽

2021 ◽

Author(s):

Shah bano ◽

Syed Adnan Shah ◽

Wakeel Ahmad ◽

Muhammad Ilyas

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Classifier ◽

Binary Code ◽

Spatial Information ◽

Detection Algorithm ◽

Support Vector ◽

Surveillance Systems ◽

Body Parts ◽

Public Places ◽

Surveillance Camera

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

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