Pattern Recognition
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Road Observation and Information Providing System for Supporting Mobility of Pedestrian
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Human model for people detection in dynamic scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
An automatic method for generating affine moment invariants
Pattern Recognition Letters
A Robust Algorithm for Shadow Removal of Foreground Detection in Video Surveillance
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
A Method for Automatic Detection of Crimes for Public Security by Using Motion Analysis
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
SVM-based ontology matching approach
International Journal of Automation and Computing
International Journal of Automation and Computing
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Video surveillance is an active research topic in computer vision. In this paper, humans and cars identification technique suitable for real time video surveillance systems is presented. The technique we proposed includes background subtraction, foreground segmentation, shadow removal, feature extraction and classification. The feature extraction of the extracted foreground objects is done via a new set of affine moment invariants based on statistics method and these were used to identify human or car. When the partial occlusion occurs, although features of full body cannot be extracted, our proposed technique extracts the features of head shoulder. Our proposed technique can identify human by extracting the human head-shoulder up to 60%---70% occlusion. Thus, it has a better classification to solve the issue of the loss of property arising from human occluded easily in practical applications. The whole system works at approximately 16---29 fps and thus it is suitable for real-time applications. The accuracy for our proposed technique in identifying human is very good, which is 98.33%, while for cars' identification, the accuracy is also good, which is 94.41%. The overall accuracy for our proposed technique in identifying human and car is at 98.04%. The experiment results show that this method is effective and has strong robustness.