New JMDF Technology Merges Traditional Matrix Decomposition and Factorization Methods for Enhanced Video Surveillance

Credit: Linhao Li

Flowchart of the proposed framework. First, the video BG is modeled by fuzzy factorization. Second, background subtraction is conducted, and then the Spatio-temporal constraints are applied to obtain the FG component. After that, the Gaussian noise in residual and the FG component update the membership degree together. Finally, the above process is iterated until convergence. ( DOI: 10.1007/s11704-022-2099-0)

« New JMDF Technology Merges Traditional Matrix Decomposition and Factorization Methods for Enhanced Video Surveillance

Credit: Linhao Li

Robustness to different noises. The source video frame and the result from the study’s JMDF are displayed in the lower right corner of the figure. The research team add Gaussian noise, speckle noise, salt and pepper noise, and Poisson noise to the source video, respectively. Except for Poisson noise, they fix the noise mean to 0, and change the noise variance, whose values are shown in the first row of each sub-figure. Then, the team plot the noisy frame examples in the second row and the results from JMDF in the third row. As the variance increases, the video gradually becomes blurred, which increases the difficulty of the detection task. This method achieves good performance even in videos with large noise variance( DOI: 10.1007/s11704-022-2099-0)

« New JMDF Technology Merges Traditional Matrix Decomposition and Factorization Methods for Enhanced Video Surveillance

Credit: Linhao Li

ADMM algorithm for JMDF-based model

« New JMDF Technology Merges Traditional Matrix Decomposition and Factorization Methods for Enhanced Video Surveillance

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