cchrisnguyen/videorpca

Robust PCA for Moving Object Detection in Video

Robust PCA for Moving Object Detection in Video

This project demonstrates the use of Robust Principal Component Analysis (RPCA) for detecting and removing moving objects from a video sequence. The RPCA algorithm decomposes a video sequence into a low-rank background matrix and a sparse moving object matrix, allowing for the detection and removal of moving objects from the background.

Requirements

• Python 3.x
• OpenCV (version 4.x or later)
• NumPy
• Scikit-learn
• Tensorboard (optional, for learning process visualization)
• Matplotlib (optional, for result visualization)

Usage

1. Clone or download the repository to your local machine.

2. Install the required packages using pip or conda:

pip install opencv-python numpy scikit-learn tensorboard matplotlib

3. Download the input video file shoppingmall.mat from the Google Drive link. Place it in the data folder.

4. To use this project, simply run the main.py script with the desired command line arguments. The available command line arguments are:

• --lambda: regularization parameter for the sparse component.
• --mu: initial value of the penalty parameter in the Augmented Lagrangian Multiplier (ALM) algorithm.
• --max_iter: maximum number of iterations for the optimization algorithm (default: 1000).
• --eps_primal: threshold for the primal error in the convex optimization problem (default: 1e-7).
• --eps_dual: theshold for the dual error in the convex optimzation problem (default: 1e-5).
• --rho: ratio of the paramter mu between two successive iterations (default: 1.6).
• --initial_sv: number of singular values to compute during the first iteration (default: 10).
• --max_mu: maximum value that mu is allowed to take. (default: 1e6).
• --verbose: whether output learning process, both printing and tensorboard (default: True).
• --save_interval:number of iterations for which the result will be saved (default: 10).

For example, to run the script with a regularization parameter of 0.1 and the max iteration of 20, use the following command:

python main.py --lambda 0.1 --max_iter 20

or

nohup python -u main.py --lambda 0.1 --max_iter 20 > log.out &

5. The resulting low-rank and sparse components will be saved as separate videos in the output folder.

Credits

This project is inspired by the papers:

1. Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 1-37.

2. Lin, Z., Chen, M., & Ma, Y. (2010). The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055.

License

This project is licensed under the MIT License.