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Kalaiselvi, S.
- An Efficient Crowd Behavior Recognition using Motion Patterns for Intelligent Video Surveillance
Authors
1 National Engineering College, Kovilpatti, IN
Source
Biometrics and Bioinformatics, Vol 7, No 1 (2015), Pagination: 23-27Abstract
An automated visual monitoring process expands from low level analysis of object detection and tracking to the interpretation of their behaviors. Analyzing human crowd is an emerging trend in intelligent video surveillance for the purpose of detecting abnormalities. Tracking every human being in a crowd and analyzing their behavior is a challenging task due to occlusions. Hence, the crowd can be handled as a group entity instead of tracking the individual in the crowd. The behavior of the crowd can be distinguished with motion patterns due to prominent spatio-temporal characteristics. The proposed system involves a systematic approach to recognize the global events in human crowd through observing motion patterns such as flow, speed and direction. Initially as a preprocessing step, background subtraction is performed to extract the foreground blobs and optical flow is estimated to obtain the velocity and direction of motion. The human crowds are then clustered based on similar direction and proximity using Adjacency Matrix based Clustering (AMC). After clustering, the centroid and orientation of the cluster are extracted inorder to represent the behavior of crowd. Finally the multiclass Support Vector Machine (SVM) is trained to correctly recognize the behavior of crowd.
Keywords
Crowd Behavior, Optical Flow, Adjacency Matrix Based Clustering, Multiclass SVM.
- Enhanced ATM Machine Using Voice Recognition to Reduce Fraudulence Rate
Authors
1 Cochin University, Kerela, IN
2 Vellore, IN
3 Chennai, IN
Source
Biometrics and Bioinformatics, Vol 4, No 7 (2012), Pagination: 330-333Abstract
This paper focuses on the implementation of voice recognition in ATM machine. The main aim is to make the disabled people use the ATM in an effective manner. This method is one of the safe recognition and cost effective system which is appropriate for the current scenario. The implementation of this system depends on three algorithm includes: Hidden state algorithm for speech rate and frequency evaluation, Pitch identification algorithm for pitch estimation of voiceprints and accent analysis algorithm for accent calculation. These proposed algorithms make the system much more secured, efficient and accurate than the other system. The advantages in the proposed voice recognition system are: The background noises and distortion in voice is reduced and the insecurity in the system is overcome.