Real-time multi-camera video analytics system on GPU

Güler, Puren
Emeksiz, Deniz
Temizel, Alptekin
Teke, Mustafa
Temizel, Tugba Taskaya
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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Citation Formats
P. Güler, D. Emeksiz, A. Temizel, M. Teke, and T. T. Temizel, “Real-time multi-camera video analytics system on GPU,” JOURNAL OF REAL-TIME IMAGE PROCESSING, pp. 457–472, 2016, Accessed: 00, 2020. [Online]. Available: