A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods

  Recently, most background modeling (BM) methods claim to achieve real-time efficiency for low-resolution and standard-definition surveillance videos. With the increasing resolutions of surveillance cameras, full high-definition (full HD) surveillance videos have become the main trend and thus processing high-resolution videos becomes a novel issue in intelligent video surveillance. In this paper, we propose a novel contentadaptive resizing framework (CARF) to boost the computation speed of BM methods in high-resolution surveillance videos. For each frame, we apply superpixels to separate the content of the frame to homogeneous and boundary sets. Two novel downsampling and upsampling layers based on the homogeneous and boundary sets are proposed. The front one downsamples highresolution frames to low-resolution frames for obtaining efficient foreground segmentation results based on BM methods. The later one upsamples the low-resolution foreground segmentation results to the original resolution frames based on the superpixels. By simultaneously coupling both layers, experimental results show that the proposed method can achieve better quantitative and qualitative results compared with state-of-the-art methods. Moreover, the computation speed of the proposed method without GPU accelerations is also significantly faster than that of the state-of-the-art methods. The source code of CARF is available at https://github.com/nchucvml/CARF.

The demo videos are available at

https://www.youtube.com/playlist?list=PLeFabaAzO2xwAr_Ya9ui8hWEtFpAieTYR

Download CARF from GitHub

    If you use the CARF codes, please cite the following paper. Chun-Rong Huang, Wei-Yun Huang, Yi-Sheng Liao, Chien-Cheng Lee, and Yu-Wei Yeh, "A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods," accepted by IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020.

Chun-Rong Huang crhuang at nchu.edu.tw

[1] Chun-Rong Huang, Wei-Yun Huang, Yi-Sheng Liao, Chien-Cheng Lee, and Yu-Wei Yeh, "A Content-Adaptive Resizing Framework for Boosting Computation Speed of Background Modeling Methods," accepted by IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020.