Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 11, Issue 6, Pages 1267-1276Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-019-01037-x
Keywords
Moving detection; SOBS; Adaptive detection; Random strategy
Categories
Funding
- Beijing education science Project [SM201810038006]
- Key Teachers for Capital University of economics and business
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The adaptability plays a significant role in moving detection. The diverse scenarios in real world still challenge this problem. Therefore, in this paper, we proposed an adaptive moving detection method, namely Adaptive Random-based Self-Organizing back- ground subtraction (ABSOBS) method. This method can adaptively extract the moving objects in various conditions and eliminate the ghost pixels simultaneously. Therefore, a robust initialization strategy is proposed to remove the noise pixels caused by the initialized frames. The proposed method uses a random- based scheme which allows the foreground pixels to up- date the neural network with a small probability. This strategy allows our algorithm to efficiently handle scene changes. Moreover, a foreground filter based on random rule is designed to eliminate the ghost pixel. More importantly, ABSOBS adopts a regulator to control the updating rate in different conditions. It makes our method easy-to-used and need not to set the parameters manually. The experiment results on various scenarios show that our method improves the detection accuracy for the SOBS and outperforms other state-of- the-art methods.
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