Behavior Recognition In Video Surveillance System For Indoor Public Areas Using Artificial Immune System
Behavior recognition and predicting the activities of people in public areas are still a major concern in image processing and artificial intelligence science. Artificial intelligence systems are widely used to extract and analyze the complicated human actions through logical and mathematical rules....
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/5384/1/FK_2008_26.pdf http://psasir.upm.edu.my/id/eprint/5384/ |
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| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | Behavior recognition and predicting the activities of people in public areas are still a major concern in image processing and artificial intelligence science. Artificial intelligence systems are widely used to extract and analyze the complicated human actions through logical and mathematical rules.
This study has explored an intelligent video surveillance system, presented by real time moving detection, object classification and interpreting the activity of the people by employing image segmentation and new approach in artificial intelligence called artificial immune system. The new system was compared with the previous methods in two level processing such as preprocessing for pixel manipulation and high level processing for behavior description. It was discovered that the new system required less processing time to apply filters in pixel level and higher data accuracy with less time complexity to generate training data and monitoring phase. This study further improved the performance of object tracking. The improvement was achieved by simplifying the previous algorithm without applying mathematical or probabilistically formulas and selects the effective filters to create a clearer foreground pixel map. Also, the robust algorithm with hands of artificial immune system rules like binary hamming shape-space and advance detector structure with fast decision making to detect three abnormal behaviors such as entering the forbidden area, standing more than threshold and running was implemented
The result obtained showed the improvement in the duration for each phase when compared with previous methods in image segmentation like mixture of Gaussian and behavior recognition like and/Or tree or neural networks. |
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