@inproceedings{cb0233bc6c144183a1635693482cd3d7,
title = "Rejection-Cascade of Gaussians: Real-Time Adaptive Background Subtraction Framework",
abstract = "Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians (CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4{\textendash}5 and 17\% average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder (VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.",
keywords = "Background subtraction, Real-time, Rejection cascade",
author = "Kiran, \{B. Ravi\} and Arindam Das and Senthil Yogamani",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 7th National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, NCVPRIPG 2019 ; Conference date: 22-12-2019 Through 24-12-2019",
year = "2020",
doi = "10.1007/978-981-15-8697-2\_25",
language = "English",
isbn = "9789811586965",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "272--281",
editor = "Babu, \{R. Venkatesh\} and Mahadeva Prasanna and Namboodiri, \{Vinay P.\}",
booktitle = "Computer Vision, Pattern Recognition, Image Processing, and Graphics - 7th National Conference, NCVPRIPG 2019, Revised Selected Papers",
}