Smart objects identification system for robotic surveillance
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 ext...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/35901/ http://irep.iium.edu.my/35901/ http://irep.iium.edu.my/35901/ http://irep.iium.edu.my/35901/1/Smart%2BObjects%2BIdentification%2BSystem%2Bfor%2BRobotic%2BSurveillance.pdf |
| Summary: | 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. |
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