Journal
JOURNAL OF REAL-TIME IMAGE PROCESSING
Volume 11, Issue 3, Pages 457-472Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11554-013-0337-2
Keywords
Video surveillance; Video analytics; Real-time; CUDA; GPU
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Funding
- Ministry of Science, Industry and Technology SAN-TEZ program [00542.STZ.2010-1]
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In this article, parallel implementation of a real-time intelligent video surveillance system on Graphics Processing Unit (GPU) is described. The system is based on background subtraction and composed of motion detection, camera sabotage detection (moved camera, out-of-focus camera and covered camera detection), abandoned object detection, and object-tracking algorithms. As the algorithms have different characteristics, their GPU implementations have different speed-up rates. Test results show that when all the algorithms run concurrently, parallelization in GPU makes the system up to 21.88 times faster than the central processing unit counterpart, enabling real-time analysis of higher number of cameras.
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