Automated visual surveillance using kernel tricks
Extensive network of multimodal surveillance and security sensors prevalent in many places. Task of simultaneously monitoring multiple images tedious and monotonous for a human. Existing algorithms involve high complexities, need significant memory and storage resources, and typically involve...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/31259/ http://irep.iium.edu.my/31259/ http://irep.iium.edu.my/31259/1/CVML13_poster.pdf http://irep.iium.edu.my/31259/4/lettre-confirmation-poster-TAREM.pdf |
| Summary: | Extensive network of multimodal surveillance and security sensors prevalent in many places.
Task of simultaneously monitoring multiple images tedious and monotonous for a human.
Existing algorithms involve high complexities, need significant memory and storage resources, and typically involve custom equipment.
We present three algorithms built using kernel machines to perform automated, real-time intruder detection in surveillance systems.
Proposed algorithms are adaptive and portable, with computational, storage and memory complexities independent of time, making them naturally suited to online use. |
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